From 6cea0c84eba0fd443f18458a182a9bc1bde0f87d Mon Sep 17 00:00:00 2001 From: Carla Oliveira Date: Sat, 3 Sep 2022 17:10:36 -0300 Subject: [PATCH 1/9] Criado usando o Colaboratory --- New-Model-From-INCT-DD-Evaluation.ipynb | 15350 ++++++++++++++++++++-- 1 file changed, 13912 insertions(+), 1438 deletions(-) diff --git a/New-Model-From-INCT-DD-Evaluation.ipynb b/New-Model-From-INCT-DD-Evaluation.ipynb index 54ea138..1a2a93c 100644 --- a/New-Model-From-INCT-DD-Evaluation.ipynb +++ b/New-Model-From-INCT-DD-Evaluation.ipynb @@ -1,1440 +1,13914 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Elaboração de um novo modelo de classificação com base nas informações de usuários avaliados pelo INCT-DD**" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WdmimB43vEnb" + }, + "source": [ + "**Elaboração de um novo modelo de classificação com base nas informações de usuários avaliados pelo INCT-DD**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "pfW-ynZ3vEne" + }, + "outputs": [], + "source": [ + "#Carrega as bibliotecas\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.tree import DecisionTreeClassifier \n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "from matplotlib import pyplot as plt\n", + "from sklearn import tree\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef, mean_squared_error, r2_score, mean_absolute_percentage_error, max_error, explained_variance_score, median_absolute_error\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neural_network import MLPClassifier, MLPRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.feature_extraction.text import TfidfTransformer\n", + "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, classification_report\n", + "import math\n", + "import statistics\n", + "import datetime\n", + "import pytz\n", + "import pickle\n", + "## NLTK (biblioteca para processamento de linguagem natural)\n", + "import nltk\n", + "from nltk.stem.rslp import RSLPStemmer ##http://www.nltk.org/howto/portuguese_en.html\n", + "\n", + "#O primeiro uso exige obter os pacotes adicionais da biblioteca descomentando as linhas a seguir\n", + "#Instala os pacotes de termos do nltk (apenas na primeira vez)\n", + "#nltk.download()\n", + "#nltk.download('rslp')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EPYB_rxhvEng" + }, + "source": [ + "**O novo modelo de classificação de bots foi construído com base nos usuários manualmente avaliados pelo INCT-DD**\n", + "\n", + "Essa escolha foi tomada considerando que esse conjunto de dados é o melhor que se possui quanto à real possibilidade de um usuário do Twitter ser um bot, não existindo bases de avaliação dentro da realidade brasileira (especialmente quanto ao português), bem como atualizadas" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "OyGwd_QQvEnh", + "outputId": "85898ffb-7dfe-4438-f556-e65ef8979cbe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1074\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0 error created_at default_profile \\\n", + "0 0 0 2009-06-30 01:05:51+00:00 1.0 \n", + "1 1 0 2019-03-09 11:29:52+00:00 True \n", + "2 2 0 2009-10-20 01:19:19+00:00 False \n", + "3 3 0 2020-05-03 19:06:46+00:00 True \n", + "4 4 0 2021-04-25 20:04:17+00:00 True \n", + "\n", + " description followers_count \\\n", + "0 0 21.0 \n", + "1 0 4192.0 \n", + "2 Feliz é a Nação cujo Deus é o Senhor! 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\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "#Busca os dados dos usuários avaliados\n", + "datafile_users = \"/content/sample_data/inct_users.csv\"\n", + "df_users = pd.read_csv(datafile_users, header = 0)\n", + "\n", + "#Preenche os valores NaN con 0 apenas para avaliação geral\n", + "df_users = df_users.fillna(0)\n", + "print(len(df_users))\n", + "#Apresenta o total de usuários avaliados\n", + "df_users.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dW6sONWcvEni" + }, + "source": [ + "**No novo modelos são consideradas apenas as informações associadas como \"É bot?\" de respotas \"Sim\" ou \"Não\"**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "UjegnTdGvEnj", + "outputId": "6592b2d9-6cdf-45ff-82b0-0531277eeca8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1074\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 não\n", + "1 não\n", + "2 não\n", + "3 sim\n", + "4 Não\n", + "Name: É Bot?, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "#Busca a classificação do INCT-DD\n", + "datafile_handles = \"/content/sample_data/handles_inct.csv\" #A classificação é a mesma da sample1\n", + "df_handles = pd.read_csv(datafile_handles, header = 0)\n", + "print(len(df_handles))\n", + "df_handles['É Bot?'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1dPPFXfivEnk" + }, + "source": [ + "**As mais recentes postagens dos usuários foram consideradas como um atributo do modelo**\n", + "\n", + "Para a classificação dos usuários, o novo modelo inclui atributos relacionados com as postagens dos usuários, na tentativa de extrair informação mais atualizada e dinâmica de sua atuação. Entretanto, os textos das postagens foram utilizados unificando seus conteúdos e extraindo informações representativas, tais como os termos mais recorrentemente utilizados, diferença no tempo das postagens e repostagens" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "YH3gaVLHvEnl", + "outputId": "e3fcc16c-c6df-487c-92c6-dd3936b8c773", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 583 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "82413\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (7,8,12,15) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0 error tweet_author tweet_author_id_str tweet_contributors \\\n", + "0 0 NaN lemathes 52253248 NaN \n", + "1 1 NaN lemathes 52253248 NaN \n", + "2 2 NaN lemathes 52253248 NaN \n", + "3 3 NaN lemathes 52253248 NaN \n", + "4 4 NaN lemathes 52253248 NaN \n", + "\n", + " tweet_created_at tweet_favorite_count tweet_favorited tweet_geo \\\n", + "0 2022-03-09 02:10:58+00:00 0.0 0.0 NaN \n", + "1 2022-03-09 02:10:12+00:00 0.0 False NaN \n", + "2 2022-03-02 21:57:17+00:00 0.0 False NaN \n", + "3 2022-03-02 16:57:51+00:00 1.0 False NaN \n", + "4 2022-03-02 16:54:56+00:00 0.0 False NaN \n", + "\n", + " tweet_hashtags tweet_id tweet_id_str tweet_is_retweet \\\n", + "0 [] 1.501380e+18 1501379987747876874 0.0 \n", + "1 [] 1.501380e+18 1501379796210757632 False \n", + "2 [] 1.499142e+18 1499141820722421760 False \n", + "3 [] 1.499066e+18 1499066467916079105 False \n", + "4 [] 1.499066e+18 1499065733086695425 False \n", + "\n", + " tweet_lang tweet_place tweet_retweeted tweet_source \\\n", + "0 pt NaN 0.0 Twitter for Android \n", + "1 pt NaN False Twitter for Android \n", + "2 pt NaN False Twitter for Android \n", + "3 pt NaN False Twitter for Android \n", + "4 pt NaN False Twitter for Android \n", + "\n", + " tweet_text \n", + "0 @LucianoHangBr Já demorou muito! \n", + "1 RT @LucianoHangBr: A vida precisa continuar e ... \n", + "2 Pq ñ mandam uma bomba na cabeça do Pudim e aca... \n", + "3 @carteiroreaca Usa máscara, quem quer e acha q... \n", + "4 @carteiroreaca Isso aí!!! 👏👏👏👏 Já demorou de m... " + ], + "text/html": [ + "\n", + "
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00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]1.501380e+1815013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!
11NaNlemathes52253248NaN2022-03-09 02:10:12+00:000.0FalseNaN[]1.501380e+181501379796210757632FalseptNaNFalseTwitter for AndroidRT @LucianoHangBr: A vida precisa continuar e ...
22NaNlemathes52253248NaN2022-03-02 21:57:17+00:000.0FalseNaN[]1.499142e+181499141820722421760FalseptNaNFalseTwitter for AndroidPq ñ mandam uma bomba na cabeça do Pudim e aca...
33NaNlemathes52253248NaN2022-03-02 16:57:51+00:001.0FalseNaN[]1.499066e+181499066467916079105FalseptNaNFalseTwitter for Android@carteiroreaca Usa máscara, quem quer e acha q...
44NaNlemathes52253248NaN2022-03-02 16:54:56+00:000.0FalseNaN[]1.499066e+181499065733086695425FalseptNaNFalseTwitter for Android@carteiroreaca Isso aí!!! 👏👏👏👏 Já demorou de m...
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52130 rows × 21 columns

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1000\n", + "\n", + "RachedJorge - 5 - 25\n", + "\n", + "lovelyfritz_ - 100000 - 1000\n", + "\n", + "sparkyviana - 2 - 17\n", + "\n", + "iEatSanie - 100000 - 1000\n", + "\n", + "ThePattryota - 100000 - 1000\n", + "\n", + "luanlino__ - 2 - 21\n", + "\n", + "Guile_Phoenix38 - 100000 - 1000\n", + "\n", + "Alexand85602241 - 100000 - 1000\n", + "\n", + "Dris16375387 - 3 - 20\n", + "\n", + "OlindaBot - 2 - 300\n", + "\n", + "ALEXAND59302288 - 0 - 8\n", + "\n", + "soovgrI - 100000 - 1000\n", + "\n", + "frustedyubin - 100000 - 1000\n", + "\n", + "ZdosMemes1 - 100000 - 1000\n", + "\n", + "teteu550 - 100000 - 1000\n", + "\n", + "SmileSwettie - 0 - 49\n", + "\n", + "Rodrigo41527015 - 0 - 6\n", + "\n", + "sarulgbt - 100000 - 1000\n", + "\n", + "NettoOlimpio - 100000 - 1000\n", + "\n", + "tsuyuws - 100000 - 1000\n", + "\n", + "javddcruel - 100000 - 1000\n", + "\n", + "Dany96486051 - 100000 - 1000\n", + "\n", + "dekub0wl - 100000 - 1000\n", + "\n", + "uai_bot - 100000 - 1000\n", + "\n", + "SER0BF - 2 - 119\n", + "\n", + "Raphael42520115 - 1 - 33\n", + "\n", + "JackBoiSpam - 2 - 192\n", + "\n", + "fdsjotapee - 100000 - 1000\n", + "\n", + "RamonCo94008505 - 8 - 213\n", + "\n", + "WendelSodr4 - 3 - 22\n", + "\n", + "MoliAveli - 5 - 19\n", + "\n", + "sooyaluar - 0 - 294\n", + "\n", + "HABITYOU91 - 100000 - 1000\n", + "\n", + "AntonioSeixasd1 - 100000 - 1000\n", + "\n", + "Khoa86465023 - 100000 - 1000\n", + "\n", + "EDWARDFOBIC - 0 - 28\n", + "\n", + "fdutra20 - 100000 - 1000\n", + "\n", + "filtersavage - 100000 - 1000\n", + "\n", + "pwrguitar - 100000 - 1000\n", + "\n", + "suliuwu - 100000 - 1000\n", + "\n", + "IFTDRK_LUA - 100000 - 1000\n", + "\n", + "h00nivxz - 100000 - 1000\n", + "\n", + "cebolonis - 100000 - 1000\n", + "\n", + "khmdior - 4 - 156\n", + "\n", + "kchoustar - 5 - 123\n", + "\n", + "UursoB - 100000 - 1000\n", + "\n", + "gusmeyo - 5 - 185\n", + "\n", + "CrisCrisDFBRA2 - 2 - 40\n", + "\n", + "soovcry - 100000 - 1000\n", + "\n", + "wtfsky_ - 100000 - 1000\n", + "\n", + "HEYT4RTAGLI - 100000 - 1000\n", + "\n", + "projeto7C0 - 0 - 0\n", + "\n", + "albani_pedropp - 100000 - 1000\n", + "\n", + "JacintaToledo - 6 - 47\n", + "\n", + "RMatos63867017 - 4 - 21\n", + "\n", + "Direita46591384 - 3 - 18\n", + "\n", + "Rogerio34212611 - 2 - 54\n", + "\n", + "JosCost00443299 - 3 - 21\n", + "\n", + "Antonio58123 - 2 - 42\n", + "\n", + "IiiVult - 4 - 33\n", + "\n", + "LuizPaiola - 20 - 3299\n", + "\n", + "oproprioeldivo - 4 - 25\n", + "\n", + "CaravanaMccoy - 8 - 41\n", + "\n", + "Plato14181684 - 2 - 16\n", + "\n", + "EuCarlosCrvg - 100000 - 1000\n", + "\n", + "NinaLuz23695256 - 5 - 26\n", + "\n", + "scris20231 - 10 - 41\n", + "\n", + "MarciaB16982788 - 0 - 19\n", + "\n", + "LucianeLazzarin - 5 - 20\n", + "\n", + "MargaretteBras5 - 100000 - 1000\n", + "\n", + "JBOlive31644311 - 2 - 21\n", + "\n", + "LiliaRRS8 - 2 - 21\n", + "\n", + "Camilo20211 - 2 - 20\n", + "\n", + "Roberso98250940 - 6 - 15\n", + "\n", + "lu_salvucci - 2 - 56\n", + "\n", + "ValmorRodrigu17 - 0 - 11\n", + "\n", + "Manuela42572532 - 4 - 38\n", + "\n", + "PauloAr90832347 - 6 - 84\n", + "\n", + "MariaRobertaAl8 - 5 - 41\n", + "\n", + "AnaSilviaBotti1 - 9 - 106\n", + "\n", + "Marly53440332 - 3 - 19\n", + "\n", + "ninalovemetal - 3 - 31\n", + "\n", + "Luka10871610 - 7 - 82\n", + "\n", + "AnaBeat34202412 - 1 - 135\n", + "\n", + "doragouvea - 2 - 52\n", + "\n", + "ganowicz_gan - 100000 - 1000\n", + "\n", + "itsjeonjkboy - 100000 - 1000\n", + "\n", + "Sidnei72007866 - 3 - 31\n", + "\n", + "AiltonAlvesBom2 - 2 - 35\n", + "\n", + "NevesJuvenil - 4 - 11\n", + "\n", + "FredericoFDias2 - 2 - 45\n", + "\n", + "JubVasconcelos - 3 - 26\n", + "\n", + "Anselmo04800217 - 100000 - 1000\n", + "\n", + "jeremiasalecri1 - 100000 - 1000\n", + "\n", + "Juracimoreira2 - 2 - 96\n", + "\n", + "zfabrogmailcom - 1 - 40\n", + "\n", + "LuizEdu29812978 - 1 - 35\n", + "\n", + "g_garc2 - 0 - 13\n", + "\n", + "RogrioG79108167 - 3 - 28\n", + "\n", + "DaviSil46494090 - 3 - 24\n", + "\n", + "lucia98624147 - 0 - 32\n", + "\n", + "MDSouza16 - 3 - 41\n", + "\n", + "silvano34982713 - 9 - 77\n", + "\n", + "NusaAlex - 5 - 72\n", + "\n", + "ParaibanoJorge - 100000 - 1000\n", + "\n", + "JairoPatriotaMG - 100000 - 1000\n", + "\n", + "MarionCobret2 - 100000 - 1000\n", + "\n", + "AVERYF4LLS - 100000 - 1000\n", + "\n", + "HugoTdeSouzaJn1 - 2 - 13\n", + "\n", + "DelfrariVinny - 5 - 30\n", + "\n", + "LucineaMariaDe1 - 0 - 16\n", + "\n", + "2Rockkk - 100000 - 1000\n", + "\n", + "Jos17846367 - 4 - 72\n", + "\n", + "Geanesa64267041 - 4 - 36\n", + "\n", + "Beto1967B - 2 - 67\n", + "\n", + "ManoelFidelis1 - 3 - 23\n", + "\n", + "ElacheElache - 8 - 61\n", + "\n", + "ROBSONB93874205 - 0 - 19\n", + "\n", + "Lilian14876478 - 0 - 15\n", + "\n", + "Geraldo35987490 - 3 - 13\n", + "\n", + "MarizMarcella - 0 - 40\n", + "\n", + "SaG9A - 100000 - 1000\n", + "\n", + "Josbrsousa - 2 - 9\n", + "\n", + "aragonez_pedro - 3 - 94\n", + "\n", + "Direito31585503 - 100000 - 1000\n", + "\n", + "IsmeniaFranco - 2 - 19\n", + "\n", + "MarcosA14278872 - 2 - 26\n", + "\n", + "RelredeS - 0 - 14\n", + "\n", + "CPER1972 - 100000 - 1000\n", + "\n", + "GersonC33316796 - 3 - 18\n", + "\n", + "ChobasB - 4 - 22\n", + "\n", + "Belfav - 0 - 23\n", + "\n", + "CruzAdrianai3 - 100000 - 1000\n", + "\n", + "sales_amaral - 5 - 19\n", + "\n", + "___DENISE___EU_ - 4 - 16\n", + "\n", + "MauroAlvesZL - 100000 - 1000\n", + "\n", + "mariasansone161 - 3 - 12\n", + "\n", + "JampaRobo - 0 - 0\n", + "\n", + "BenicioJose0577 - 100000 - 1000\n", + "\n", + "eloirwschutz - 4 - 22\n", + "\n", + "Dioguinho141 - 16 - 1796\n", + "\n", + "CRISTIA33075520 - 1 - 25\n", + "\n", + "AlziraAlmeida11 - 4 - 23\n", + "\n", + "lcrive - 100000 - 1000\n", + "\n", + "Carloso74139217 - 100000 - 1000\n", + "\n", + "DouglasCorraRi1 - 5 - 33\n", + "\n", + "sanzio_eduardo - 100000 - 1000\n", + "\n", + "hamarissi1 - 4 - 60\n", + "\n", + "Medeirosjz - 100000 - 1000\n", + "\n", + "Antonio12671876 - 100000 - 1000\n", + "\n", + "ArtInovar - 3 - 11\n", + "\n", + "IvoSantanaMarc1 - 4 - 15\n", + "\n", + "Brasil68195790 - 100000 - 1000\n", + "\n", + "Dri65B - 100000 - 1000\n", + "\n", + "SuperBolsomini1 - 100000 - 1000\n", + "\n", + "mfpecanha1 - 100000 - 1000\n", + "\n", + "arqueira_a - 100000 - 1000\n", + "\n", + "CludiaTanaka2 - 100000 - 1000\n", + "\n", + "Helena_Cabello1 - 2 - 11\n", + "\n", + "VeigaJuscelina - 100000 - 1000\n", + "\n", + "owoguinho - 100000 - 1000\n", + "\n", + "marilia_goretti - 0 - 21\n", + "\n", + "LuizAugustoPai4 - 6 - 38\n", + "\n", + "chocopoemlate16 - 1 - 16\n", + "\n", + "Joonbabykoya - 100000 - 1000\n", + "\n", + "zoldyevvil - 100000 - 1000\n", + "\n", + "predadoalfa - 6 - 219\n", + "\n", + "FePatriota1 - 3 - 19\n", + "\n", + "NandaAndretto - 100000 - 1000\n", + "\n", + "safetyjm - 100000 - 1000\n", + "\n", + "CarlosG82785363 - 1 - 60\n", + "\n", + "KP62A - 5 - 92\n", + "\n", + "marstwolf - 0 - 8123.0\n", + "\n", + "Marcos_11_66 - 0 - 37\n", + "\n", + "Rosiveti1 - 3 - 10\n", + "\n", + "uzusaske - 100000 - 1000\n", + "\n", + "vhsmessy - 100000 - 1000\n", + "\n", + "JMBBrasil - 100000 - 1000\n", + "\n", + "baia_canuto - 3 - 32\n", + "\n", + "pjmackerman - 4 - 16340\n", + "\n", + "EN30A - 100000 - 1000\n", + "\n", + "clara_kess - 3 - 94\n", + "\n", + "CesarNi85939384 - 3 - 10\n", + "\n", + "CHRBRYSHOR - 100000 - 1000\n", + "\n", + "PauloRo49195361 - 0 - 15\n", + "\n", + "AndrePenteado4 - 100000 - 1000\n", + "\n", + "Marina92011959 - 2 - 39\n", + "\n", + "Marcos_28_11_66 - 0 - 9\n", + "\n", + "bnqzyy_jkv - 100000 - 1000\n", + "\n", + "FATIMAC75843178 - 2 - 9\n", + "\n" + ] + } + ], + "source": [ + "#Incluir uma dedida da distancia temporal entre twittes (mediana e mínimo)\n", + "df_handles['Tempo mediano'] = np.array(len(df_handles))\n", + "df_handles['Tempo menor'] = np.array(len(df_handles))\n", + "iuser = 0\n", + "for user in df_handles['handle']:\n", + " df_temp = df_timeline[df_timeline['tweet_author'] == user]\n", + " itweet = 0\n", + " menor = 100000\n", + " difs = list()\n", + " tweet_date_prev = None\n", + " for tweet in df_temp['tweet_created_at']:\n", + " tweet_date = pd.to_datetime(pd.to_datetime(tweet).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"))\n", + " if itweet > 0:\n", + " dif = (tweet_date_prev - tweet_date).seconds\n", + " if dif < menor:\n", + " menor = dif\n", + " difs.append(dif)\n", + " else:\n", + " tweet_date_prev = tweet_date\n", + " tweet_date_prev = tweet_date\n", + " itweet += 1\n", + " if len(difs) > 0:\n", + " mediana = statistics.median(difs)\n", + " else:\n", + " mediana = 1000\n", + " print(user + ' - ' + str(menor) + ' - ' + str(mediana)+'\\n')\n", + " df_handles['Tempo mediano'][iuser] = mediana\n", + " df_handles['Tempo menor'][iuser] = menor\n", + " iuser += 1\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BG-iNlU3vEnq" + }, + "source": [ + "**Os dados inicialmente tratados são reunidos com a classificação dada pelo INCT-DD**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ppTFMTsTvEnq" + }, + "outputs": [], + "source": [ + "#Reune os dados do usuário com a classificação\n", + "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", + "print(len(df_result_merge))\n", + "df_result_merge.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vXb1iLC3vEnq" + }, + "source": [ + "**Os dados das postagens foram reunidos para a extração de informações representativas**\n", + "\n", + "Para viabilizar o treinamento do modelo, os dados por postagens foram convertidos em conjuntos por usuário (autor do tweet, e a representação foi dada por informações sumarizadas ou probabilísticas, por exemplo, as hashtags mais utilizadas ou o percentual de postagens realizadas a partir do Android, iPhone ou Web." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "uJXpyQCrvEnr", + "outputId": "c037e0e0-105c-4414-edad-b3b4fc803a4d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " tweet_author tweet_text\n", + "0 100_bolsonaro @OracoesB @wander_fabricio @DinhaCarvalho8 #Bo...\n", + "1 13valber1 RT @leandroruschel: Tente encontrar na extrema...\n", + "2 1976Mnc RT @MinEconomia: “Nós estamos assistindo a uma...\n", + "3 ACamargo241 RT @juliovschneider: Se liga na viatura daqui ...\n", + "4 AControld Carro Pajero TR4 4X4 Automatica, podendo sair ..." + ], + "text/html": [ + "\n", + "
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tweet_authortweet_text
0100_bolsonaro@OracoesB @wander_fabricio @DinhaCarvalho8 #Bo...
113valber1RT @leandroruschel: Tente encontrar na extrema...
21976MncRT @MinEconomia: “Nós estamos assistindo a uma...
3ACamargo241RT @juliovschneider: Se liga na viatura daqui ...
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tweet_authortweet_hashtags
0100_bolsonaro['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...
113valber1[], [], [], [], [], [], [], [], [], [], [], []...
21976Mnc[], [], [], [], [], [], ['PLP235NÃO'], [], ['P...
3ACamargo241[], [], [], [], [], [], [], [], [], [], [], []...
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tweet_authortweet_source
0100_bolsonaroTwitter Web App, Twitter Web App, Twitter Web ...
113valber1Twitter for Android, Twitter for Android, Twit...
21976MncTwitter for iPhone, Twitter for iPhone, Twitte...
3ACamargo241Twitter for Android, Twitter for Android, Twit...
4AControldTwitter Web App, Twitter Web App, Twitter Web ...
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tweet_authorretweet_tratado
0100_bolsonaronão, não, não, não, não, não, não, não, não, n...
113valber1não, não, não, não, não, não, não, não, não, n...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "#Reune as informações de twettes que são retweets\n", + "df_result_retweet = df_timeline.groupby('tweet_author').agg({'retweet_tratado':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_retweet.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "baeAt5qkvEns", + "outputId": "81abe026-7799-4438-c257-e998f209a493", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " tweet_author tweet_com_rt_tratado\n", + "0 100_bolsonaro não, não, sim, não, não, sim, sim, sim, não, n...\n", + "1 13valber1 sim, sim, sim, sim, não, não, não, não, não, n...\n", + "2 1976Mnc sim, sim, não, não, sim, sim, não, sim, sim, s...\n", + "3 ACamargo241 sim, sim, sim, sim, sim, não, sim, sim, sim, s...\n", + "4 AControld não, não, não, não, não, não, não, não, não, n..." + ], + "text/html": [ + "\n", + "
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tweet_authortweet_com_rt_tratado
0100_bolsonaronão, não, sim, não, não, sim, sim, sim, não, n...
113valber1sim, sim, sim, sim, não, não, não, não, não, n...
21976Mncsim, sim, não, não, sim, sim, não, sim, sim, s...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "#Reune as informações de twettes com RT\n", + "df_result_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_tweet_com_rt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "zkPS0tjzvEns", + "outputId": "6afd7b6c-620a-40ea-a514-438ef6e44eb0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " tweet_author retweet_e_tweet_com_rt_tratado\n", + "0 100_bolsonaro não, não, sim, não, não, sim, sim, sim, não, n...\n", + "1 13valber1 sim, sim, sim, sim, não, não, não, não, não, n...\n", + "2 1976Mnc sim, sim, não, não, sim, sim, não, sim, sim, s...\n", + "3 ACamargo241 sim, sim, sim, sim, sim, sim, sim, sim, sim, s...\n", + "4 AControld não, não, não, não, não, não, não, não, não, n..." + ], + "text/html": [ + "\n", + "
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tweet_authorretweet_e_tweet_com_rt_tratado
0100_bolsonaronão, não, sim, não, não, sim, sim, sim, não, n...
113valber1sim, sim, sim, sim, não, não, não, não, não, n...
21976Mncsim, sim, não, não, sim, sim, não, sim, sim, s...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "#Reune as informações da junção de retweets e tweets com rt\n", + "df_result_retweet_e_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'retweet_e_tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_retweet_e_tweet_com_rt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "ZwA3QA7dvEns", + "outputId": "d9408e22-59e5-4e39-cfed-248a5789f0f6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", + " \n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", + " \n" + ] + } + ], + "source": [ + "#Reune os dados (merge) do usuários, suas avaliações com texto dos tweets, as hashtags, as fontes e os retweets\n", + "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_hashtags, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_source, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_retweet, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_retweet_e_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "DdtIwKDhvEnt", + "outputId": "f6951d23-f83e-46ab-b7a2-6010c3476c8f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 699 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "834\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p \\\n", + "0 0 1 https://twitter.com/@lemathes 0000.csv \n", + "1 1 2 https://twitter.com/@Maurcio98905595 0000.csv \n", + "2 2 3 https://twitter.com/@LunViana 0000.csv \n", + "3 3 4 https://twitter.com/@felipeleixas 0000.csv \n", + "4 4 5 https://twitter.com/@JoseCar41451194 0000.csv \n", + "\n", + " É Bot? Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não não se aplica NaN \n", + "1 não não se aplica NaN \n", + "2 não não se aplica NaN \n", + "3 sim Publicar hashtags Atacar \n", + "4 Não não se aplica NaN \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 sim sim \n", + "4 não não \n", + "\n", + " Parece só Retweetar? ... tweet_author_y \\\n", + "0 não ... lemathes \n", + "1 não ... Maurcio98905595 \n", + "2 não ... LunViana \n", + "3 não ... felipeleixas \n", + "4 não ... JoseCar41451194 \n", + "\n", + " tweet_hashtags tweet_author_x \\\n", + "0 [], [], [], [], [], [], [], [], [], [], [], []... lemathes \n", + "1 [], [], [], [], [], [], [], [], [], [], [], []... Maurcio98905595 \n", + "2 [], [], [], [], [], [], [], [], [], [], [], []... LunViana \n", + "3 [], ['EuApoioVotoImpresso'], [], ['GloboLixo']... felipeleixas \n", + "4 [], [], [], [], [], [], [], [], [], [], ['OsPi... JoseCar41451194 \n", + "\n", + " tweet_source tweet_author_y \\\n", + "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", + "1 Twitter for Android, Twitter Web App, Twitter ... Maurcio98905595 \n", + "2 Twitter for iPhone, Twitter for Android, Twitt... LunViana \n", + "3 Twitter for Android, Twitter for Android, Twit... felipeleixas \n", + "4 Twitter for iPhone, Twitter for iPhone, Twitte... JoseCar41451194 \n", + "\n", + " retweet_tratado tweet_author_x \\\n", + "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", + "1 sim, sim, não, sim, sim, sim, sim, não, sim, s... Maurcio98905595 \n", + "2 não, não, não, não, sim, não, não, não, não, n... LunViana \n", + "3 não, não, não, não, não, não, não, não, não, n... felipeleixas \n", + "4 não, não, não, não, não, não, não, não, não, n... JoseCar41451194 \n", + "\n", + " tweet_com_rt_tratado tweet_author_y \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "1 não, não, sim, não, não, sim, não, sim, não, n... Maurcio98905595 \n", + "2 sim, sim, sim, sim, não, sim, sim, sim, sim, s... LunViana \n", + "3 não, não, não, não, sim, não, não, não, não, n... felipeleixas \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... JoseCar41451194 \n", + "\n", + " retweet_e_tweet_com_rt_tratado \n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... \n", + "1 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "2 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "3 não, não, não, não, sim, não, não, não, não, n... \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "\n", + "[5 rows x 46 columns]" + ], + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_ytweet_hashtagstweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratado
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595[], [], [], [], [], [], [], [], [], [], [], []...Maurcio98905595Twitter for Android, Twitter Web App, Twitter ...Maurcio98905595sim, sim, não, sim, sim, sim, sim, não, sim, s...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunViana[], [], [], [], [], [], [], [], [], [], [], []...LunVianaTwitter for iPhone, Twitter for Android, Twitt...LunViananão, não, não, não, sim, não, não, não, não, n...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixas[], ['EuApoioVotoImpresso'], [], ['GloboLixo']...felipeleixasTwitter for Android, Twitter for Android, Twit...felipeleixasnão, não, não, não, não, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194[], [], [], [], [], [], [], [], [], [], ['OsPi...JoseCar41451194Twitter for iPhone, Twitter for iPhone, Twitte...JoseCar41451194não, não, não, não, não, não, não, não, não, n...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "#Exibe parte dos resultados da junção (nem todos os usuários ainda estão ativos e número de amostras diminui)\n", + "print(len(df_result_merge))\n", + "df_result_merge.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T4Eyp5jEvEnt" + }, + "source": [ + "**A classificação dos usuários foi padronizada para 0 - Não Bot e 1 - Bot**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "-6hG03d0vEnt", + "outputId": "d8349998-1807-4218-de1f-01d438ff788d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 0\n", + "Name: É Bot?, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", + "df = df_result_merge\n", + "y = df['É Bot?'].apply(lambda x: 1 if (x == 'Sim' or x == 'sim') else 0)\n", + "y.reset_index(drop=True, inplace=True)\n", + "y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tsho3SNYvEnt" + }, + "outputs": [], + "source": [ + "##Seleciona as colunas para o conjunto X\n", + "#feature_cols = ['tweet_text'] #,'tweet_source','tweet_hashtags'\n", + "#x = df['tweet_text']\n", + "#x.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Ds8AtqBvEnt" + }, + "source": [ + "** [Classficando apenas pelo texto dos Twittes (NLTK)] **" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nVJ-KWXJvEnt" + }, + "outputs": [], + "source": [ + "##Prepara o conjunto de dados para treinamento e teste\n", + "#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "ifa_JwZuvEnu", + "outputId": "9d7bbbde-16a2-41b1-af44-bd562e94eb03", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 762 + } + }, + "outputs": [ + { + "output_type": "error", + "ename": "LookupError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mLookupError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m##Método para vetorizar e contabilizar os termos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstemmer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRSLPStemmer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mStemmedCountVectorizerRSLPS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCountVectorizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbuild_analyzer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0manalyzer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStemmedCountVectorizerRSLPS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_analyzer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nltk/stem/rslp.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_rule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"step0.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m 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\u001b[0;36m70\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 582\u001b[0m \u001b[0mresource_not_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"\\n{sep}\\n{msg}\\n{sep}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresource_not_found\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mLookupError\u001b[0m: \n**********************************************************************\n Resource \u001b[93mrslp\u001b[0m not found.\n Please use the NLTK Downloader to obtain the resource:\n\n \u001b[31m>>> import nltk\n >>> nltk.download('rslp')\n \u001b[0m\n For more information see: https://www.nltk.org/data.html\n\n Attempted to load \u001b[93mstemmers/rslp/step0.pt\u001b[0m\n\n Searched in:\n - '/root/nltk_data'\n - '/usr/nltk_data'\n - '/usr/share/nltk_data'\n - '/usr/lib/nltk_data'\n - '/usr/share/nltk_data'\n - '/usr/local/share/nltk_data'\n - '/usr/lib/nltk_data'\n - '/usr/local/lib/nltk_data'\n - ''\n**********************************************************************\n" + ] + } + ], + "source": [ + "##Método para vetorizar e contabilizar os termos\n", + "stemmer = nltk.stem.RSLPStemmer()\n", + "class StemmedCountVectorizerRSLPS(CountVectorizer):\n", + " def build_analyzer(self):\n", + " analyzer = super(StemmedCountVectorizerRSLPS, self).build_analyzer()\n", + " return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n", + "stemmed_count_vect = StemmedCountVectorizerRSLPS(stop_words=nltk.corpus.stopwords.words('portuguese'))\n", + "tfidf_transformer = TfidfTransformer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1BTPUUcsvEnu" + }, + "outputs": [], + "source": [ + "##Pipeline para extrair as informaçoes e classificar com base no texto (pode ser usado ANN ou MNB [MultinomialNB(fit_prior=False)])\n", + "#text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),\n", + "# ('tfidf', TfidfTransformer()),\n", + "# ('mnb', MLPClassifier(random_state=1, max_iter=600, activation='relu',solver='adam')),\n", + "#])\n", + "#text_mnb_stemmed = text_mnb_stemmed.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xyrFTNw-vEnu" + }, + "outputs": [], + "source": [ + "#text_mnb_stemmed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "teRgHViCvEnu" + }, + "outputs": [], + "source": [ + "##Avalia a classificação\n", + "#predicted_mnb_stemmed = text_mnb_stemmed.predict(x_test)\n", + "#np.mean(predicted_mnb_stemmed == y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PI4Z0JWlvEnu" + }, + "source": [ + "**Os atributos do treinamentos envolvem diversos fatores**\n", + "\n", + "Uma das etapas mais critícas da modelagem é a definição dos atributos que representam o cenário real, nesse sentido foram incluídas o máximo de variáveis que pudessem representar um usuário e suas atividades na rede, desde o tamanho do login escolhido até o tempo mínimo entre suas postagens. Na sequência são realizadas as atividades de extração, tratamento e junção dessas informações como atributos do conjunto de treinamento do modelo." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "BqT8a9b1vEnv", + "outputId": "a9610736-6458-4564-8a95-b2045b36965b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Unnamed: 0_x', 'Unnamed: 0.1', 'tabelaAmostra', 'p', 'É Bot?',\n", + " 'Se você fosse atribuir uma função ao bot, qual seria?', 'Função #2',\n", + " 'Comportamento agressivo?',\n", + " 'Comportamento repetitivo com # ou menções?', 'Parece só Retweetar?',\n", + " 'Só compartilha links?', 'Só faz comentários?',\n", + " 'Enaltece muito outros usuários?', 'Faz muito uso de emojis?',\n", + " 'Tem muitos posts sem textos?', 'Unnamed: 14', 'handle',\n", + " 'Tempo mediano', 'Tempo menor', 'Unnamed: 0_y', 'error', 'created_at',\n", + " 'default_profile', 'description', 'followers_count', 'friends_count',\n", + " 'lang', 'location', 'name', 'profile_image', 'twitter_id',\n", + " 'twitter_is_protected', 'verified', 'withheld_in_countries',\n", + " 'tweet_author_x', 'tweet_text', 'tweet_author_y', 'tweet_hashtags',\n", + " 'tweet_author_x', 'tweet_source', 'tweet_author_y', 'retweet_tratado',\n", + " 'tweet_author_x', 'tweet_com_rt_tratado', 'tweet_author_y',\n", + " 'retweet_e_tweet_com_rt_tratado'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "df.columns #df é o conjunto completo de dados, já com os twittes-hashtags-sources-retweets em campos únicos" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "NB5JSYG7vEnv", + "outputId": "2c65855f-1221-49d5-a093-36e98bd6b54d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 681 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p \\\n", + "0 0 1 https://twitter.com/@lemathes 0000.csv \n", + "1 1 2 https://twitter.com/@Maurcio98905595 0000.csv \n", + "2 2 3 https://twitter.com/@LunViana 0000.csv \n", + "3 3 4 https://twitter.com/@felipeleixas 0000.csv \n", + "4 4 5 https://twitter.com/@JoseCar41451194 0000.csv \n", + "\n", + " É Bot? Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não não se aplica NaN \n", + "1 não não se aplica NaN \n", + "2 não não se aplica NaN \n", + "3 sim Publicar hashtags Atacar \n", + "4 Não não se aplica NaN \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 sim sim \n", + "4 não não \n", + "\n", + " Parece só Retweetar? ... tweet_author_y \\\n", + "0 não ... lemathes \n", + "1 não ... Maurcio98905595 \n", + "2 não ... LunViana \n", + "3 não ... felipeleixas \n", + "4 não ... JoseCar41451194 \n", + "\n", + " tweet_hashtags tweet_author_x \\\n", + "0 [], [], [], [], [], [], [], [], [], [], [], []... lemathes \n", + "1 [], [], [], [], [], [], [], [], [], [], [], []... Maurcio98905595 \n", + "2 [], [], [], [], [], [], [], [], [], [], [], []... LunViana \n", + "3 [], ['EuApoioVotoImpresso'], [], ['GloboLixo']... felipeleixas \n", + "4 [], [], [], [], [], [], [], [], [], [], ['OsPi... JoseCar41451194 \n", + "\n", + " tweet_source tweet_author_y \\\n", + "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", + "1 Twitter for Android, Twitter Web App, Twitter ... Maurcio98905595 \n", + "2 Twitter for iPhone, Twitter for Android, Twitt... LunViana \n", + "3 Twitter for Android, Twitter for Android, Twit... felipeleixas \n", + "4 Twitter for iPhone, Twitter for iPhone, Twitte... JoseCar41451194 \n", + "\n", + " retweet_tratado tweet_author_x \\\n", + "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", + "1 sim, sim, não, sim, sim, sim, sim, não, sim, s... Maurcio98905595 \n", + "2 não, não, não, não, sim, não, não, não, não, n... LunViana \n", + "3 não, não, não, não, não, não, não, não, não, n... felipeleixas \n", + "4 não, não, não, não, não, não, não, não, não, n... JoseCar41451194 \n", + "\n", + " tweet_com_rt_tratado tweet_author_y \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "1 não, não, sim, não, não, sim, não, sim, não, n... Maurcio98905595 \n", + "2 sim, sim, sim, sim, não, sim, sim, sim, sim, s... LunViana \n", + "3 não, não, não, não, sim, não, não, não, não, n... felipeleixas \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... JoseCar41451194 \n", + "\n", + " retweet_e_tweet_com_rt_tratado \n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... \n", + "1 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "2 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "3 não, não, não, não, sim, não, não, não, não, n... \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "\n", + "[5 rows x 46 columns]" + ], + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_ytweet_hashtagstweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratado
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595[], [], [], [], [], [], [], [], [], [], [], []...Maurcio98905595Twitter for Android, Twitter Web App, Twitter ...Maurcio98905595sim, sim, não, sim, sim, sim, sim, não, sim, s...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunViana[], [], [], [], [], [], [], [], [], [], [], []...LunVianaTwitter for iPhone, Twitter for Android, Twitt...LunViananão, não, não, não, sim, não, não, não, não, n...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixas[], ['EuApoioVotoImpresso'], [], ['GloboLixo']...felipeleixasTwitter for Android, Twitter for Android, Twit...felipeleixasnão, não, não, não, não, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194[], [], [], [], [], [], [], [], [], [], ['OsPi...JoseCar41451194Twitter for iPhone, Twitter for iPhone, Twitte...JoseCar41451194não, não, não, não, não, não, não, não, não, n...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 37 + } + ], + "source": [ + "x['Fonte de Android'] = np.array(list(fonte_android))\n", + "x['Fonte de iPhone'] = np.array(list(fonte_iphone))\n", + "x['Fonte de Web'] = np.array(list(fonte_web))\n", + "x = x.fillna(0)\n", + "x.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "SYxSo6k5vEnx", + "outputId": "c99e1bb3-108c-4fa1-a9dd-41b3aeb0198e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.641682\n", + "std 0.463189\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 1.000000\n", + "Name: Fonte de Android, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "#Avaliação geral das diferentes fontes\n", + "x['Fonte de Android'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "PTtW4jOvvEnx", + "outputId": "1ffbf590-b56b-4af5-9321-5787bc5a1d31", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.198877\n", + "std 0.393385\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + "Name: Fonte de iPhone, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "x['Fonte de iPhone'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "iIFeXnIQvEnx", + "outputId": "32dc9885-58e4-4200-ae12-5e95c420dab6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.149848\n", + "std 0.330788\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + "Name: Fonte de Web, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ], + "source": [ + "x['Fonte de Web'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "jE-W1fivvEnx", + "outputId": "aae4d7b0-ff4b-42ae-a435-0ba3b727f635", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 não, não, não, não, não, não, não, não, não, n...\n", + "1 sim, sim, não, sim, sim, sim, sim, não, sim, s...\n", + "2 não, não, não, não, sim, não, não, não, não, n...\n", + "3 não, não, não, não, não, não, não, não, não, n...\n", + "4 não, não, não, não, não, não, não, não, não, n...\n", + "Name: retweet_tratado, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "#Inclui a informação do retweet\n", + "df['retweet_tratado'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "7lcoFmwvvEny" + }, + "outputs": [], + "source": [ + "retweet_tratado = df['retweet_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['retweet_tratado_media'] = np.array(list(retweet_tratado))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "1uXOoePGvEny" + }, + "outputs": [], + "source": [ + "tweet_com_rt = df['tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['tweet_com_rt_tratado_media'] = np.array(list(tweet_com_rt))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "6KiugEWIvEny" + }, + "outputs": [], + "source": [ + "retweet_e_tweet_com_rt = df['retweet_e_tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['retweet_e_tweet_com_rt_tratado_media'] = np.array(list(retweet_e_tweet_com_rt))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "OSyDe2swvEny" + }, + "outputs": [], + "source": [ + "x_novo = x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZAQWrF-rvEny" + }, + "outputs": [], + "source": [ + "##Inclui os textos dos twittes (NLTK)\n", + "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", + "#tfidf_transformer = TfidfTransformer()\n", + "#x_tfidf = tfidf_transformer.fit_transform(st)\n", + "#x_tfidf\n", + "#x_novo = x.join(pd.DataFrame(x_tfidf.todense()))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "oYytkQlWvEny", + "outputId": "aeb433eb-2e11-4ad3-faf7-5e35e8b1befa", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(834, 15)" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "x_novo.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "0Zs-qHPsvEnz", + "outputId": "58333519-2c2a-4262-bcd3-8034b3749240", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " followers_count friends_count Tempo mediano Tempo menor \\\n", + "0 21.0 108.0 1917 16 \n", + "1 4192.0 4886.0 22 1 \n", + "2 1341.0 1854.0 34 2 \n", + "3 2.0 31.0 40791 141 \n", + "4 10.0 21.0 584 9 \n", + "\n", + " Quantidade hashtags Quantidade hashtags media Digitos no username \\\n", + "0 13 0.130000 0 \n", + "1 2 0.020000 8 \n", + "2 6 0.060000 0 \n", + "3 20 0.425532 0 \n", + "4 10 0.100000 8 \n", + "\n", + " Tamanho do username Tamanho do nome Fonte de Android Fonte de iPhone \\\n", + "0 8 14 1.00 0.00 \n", + "1 15 13 0.24 0.00 \n", + "2 8 7 0.18 0.82 \n", + "3 12 6 1.00 0.00 \n", + "4 15 34 0.00 1.00 \n", + "\n", + " Fonte de Web retweet_tratado_media tweet_com_rt_tratado_media \\\n", + "0 0.00 0.10 0.750000 \n", + "1 0.76 0.54 0.520000 \n", + "2 0.00 0.08 0.840000 \n", + "3 0.00 0.00 0.042553 \n", + "4 0.00 0.00 0.940000 \n", + "\n", + " retweet_e_tweet_com_rt_tratado_media \n", + "0 0.840000 \n", + "1 0.970000 \n", + "2 0.910000 \n", + "3 0.042553 \n", + "4 0.940000 " + ], + "text/html": [ + "\n", + "
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Webretweet_tratado_mediatweet_com_rt_tratado_mediaretweet_e_tweet_com_rt_tratado_media
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 47 + } + ], + "source": [ + "x_novo.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ds5zbJqWvEnz" + }, + "source": [ + "**Com o primeiro conjunto de atributos formado é possível separar o conjunto de dados em treinamento e teste para a elaboração do modelo**" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "dZbiiGhAvEnz" + }, + "outputs": [], + "source": [ + "#Cria um modelo de classificação para o conjunto completo\n", + "x_train, x_test, y_train, y_test = train_test_split(x_novo, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "zKaaJDpxvEnz", + "outputId": "b0f7f76e-7aa1-4051-a7fa-a40cc47fd80d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.7330677290836654" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "np.mean(y_pred == y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a14V0FEnvEnz" + }, + "outputs": [], + "source": [ + "##Seleciona os atributos mais \"importantes\"\n", + "#x_new = SelectKBest(chi2, k=20).fit_transform(x_novo, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GLO1GeHovEn2" + }, + "outputs": [], + "source": [ + "#x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "id": "cuuGpOcdvEn3", + "outputId": "018c33e6-a500-4beb-ce67-32c16b00f641", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: 0.7330677290836654 | Balanced accuracy: 0.6958582834331337\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 49, 35],\n", + " [ 32, 135]])" + ] + }, + "metadata": {}, + "execution_count": 50 + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "O5PS2y9hvEn3", + "outputId": "d8afa74f-89f0-447a-fba3-a1d46aefa6f9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.60 0.58 0.59 84\n", + " 1 0.79 0.81 0.80 167\n", + "\n", + " accuracy 0.73 251\n", + " macro avg 0.70 0.70 0.70 251\n", + "weighted avg 0.73 0.73 0.73 251\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "0y_Y_7uQvEn3", + "outputId": "a613f372-240a-4c7e-9d60-f8dea386f305", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: 0.7250996015936255 | Balanced accuracy: 0.6691616766467066\n" + ] + } + ], + "source": [ + "#Classificação com RNA\n", + "classifier = MLPClassifier(max_iter=1200, random_state=1, activation='tanh', solver='adam') #activation: logistic, relu, tanh, identity | solver: lbfgs, sgd, adam\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ki86QVDAvEn3" + }, + "source": [ + "**Informações de trend topics**\n", + "\n", + "Outra informação que se mostrou de relevância ao longo do trabalho de modelagem foi a relação das postagens de bots com as menções e hashtags listadas nos mais atuais 'trend topics', ou seja, o aparente uso de termos altamente utilizados no momento para possivelmente alavancar a visibilidade da postagem.\n", + "\n", + "Para averiguar essa possibilidade, um sistema de monitoramento dos tópicos mais mencionados foi criado e cada postagem coletada do usuário foi confrontado com os 'trend topics' do período mais próximo. Esse confrontamento gerou um percentual de uso desses tópicos nas postagens dos usuários." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "jnPs1tG6vEn3", + "outputId": "7017c937-9b8b-44a5-e1ab-31c8e3c83756", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2680\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " trending_id trend_date_time trend user1_id \\\n", + "0 1 2021-12-03 21:03:31.034742 #HappyBirthdayJin 0 \n", + "1 2 2021-12-03 21:03:31.286371 suga 28431722 \n", + "2 3 2021-12-03 21:03:31.417346 #JINDAY 132699857 \n", + "3 4 2021-12-03 21:03:31.527791 #playplusmudo 0 \n", + "4 5 2021-12-03 21:03:31.720859 TE AMAMOS DAYANE MELLO 34590687 \n", + "\n", + " tweet1 user2_id \\\n", + "0 - 0 \n", + "1 Começou!\\n\\nEles estão todos de terno e sentad... 28431722 \n", + "2 REIZINHO! 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trending_idtrend_date_timetrenduser1_idtweet1user2_idtweet2user3_idtweet3user4_idtweet4user5_idtweet5Trend Date Time Convertido
012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-2021-12-03
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-2021-12-03
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-2021-12-03
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-2021-12-03
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Caso esteja presente acumulou-se essa ocorrência, finalizando com a ocorrência de uso de uma trend por cada tweet.\n", + "Este trecho demanda de melhorias em desempenho e na inclusão de restrições que reduzam o tempo de ocorrência da trend para mais próximo do tweet." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "iR3IPD8jvEn4", + "outputId": "a29e7b87-ab2e-46d3-e773-6a1e0232a492", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n", + "19\n", + "20\n", + "21\n", + "22\n", + "23\n", + "24\n", + "25\n", + "26\n", + "27\n", + "28\n", + "29\n", + "30\n", + "31\n", + "32\n", + "33\n", + "34\n", + "35\n", + "36\n", + "37\n", + "38\n", + "39\n", + "40\n", + "41\n", + "42\n", + "43\n", + 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pd.to_datetime(pd.to_datetime(trend).strftime(\"%Y-%m-%d\"))\n", + " if trend_date <= tweet_date.tz_convert(None):\n", + " if df_timeline['tweet_text'][itweet].find(df_trends['trend'][itrend]) != -1: \n", + " df_timeline['Numero de trendings'][itweet] = df_timeline['Numero de trendings'][itweet] + 1\n", + " itrend += 1\n", + " print(itweet) \n", + " itweet += 1 " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IIb6GW_ZvEn4" + }, + "source": [ + "Para cada tweet foi armazenados o número de trend topics encontrado." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ulw5xkusvEn4" + }, + "outputs": [], + "source": [ + "df_timeline[df_timeline['Numero de trendings'] > 0].describe()\n", + "df_timeline['Numero de trendings'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XbYCwlmnvEn4" + }, + "outputs": [], + "source": [ + "df_timeline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oBqtGHSevEn4" + }, + "source": [ + "As quantidades de trendings utilizadas em cada tweet foram agrupados por autor (usuário), assim foram incluídos na base de treinamento o número de trendings utilizadas, a média de trendings por tweet desse autor e o número máximo de trendings usado em um mesmo tweet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VIZBn0qZvEn4" + }, + "outputs": [], + "source": [ + "#Reune as informações de trends nos tweets por author\n", + "df_result_trend = df_timeline.groupby('tweet_author').agg({'Numero de trendings':lambda col: sum(col)/len(col)}).reset_index()\n", + "df_result_trend_max = df_timeline.groupby('tweet_author').agg({'Numero de trendings':lambda col: max(col)}).reset_index()\n", + "df_result_trend['trends_media'] = df_result_trend['Numero de trendings']\n", + "df_result_trend_max['trends_max'] = df_result_trend_max['Numero de trendings']\n", + "df_result_trend_max" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "03RGOP9PvEn4" + }, + "outputs": [], + "source": [ + "df_handles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RxGxu0y1vEn5" + }, + "outputs": [], + "source": [ + "df_trends.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SRCJH5_PvEn5" + }, + "outputs": [], + "source": [ + "trends_unique = df_trends.trend.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_TKLTrgQvEn5" + }, + "outputs": [], + "source": [ + "df_result_merge.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FQ6AP52GvEn5" + }, + "source": [ + "Os valores referentes aos trendings do usuário são reunidos (\"merged\") com os dados gerais do usuário" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XRayUSq0vEn5" + }, + "outputs": [], + "source": [ + "df_result_merge = pd.merge(df_result_merge,df_result_trend, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_trend_max, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JZNurGgwvEn5" + }, + "outputs": [], + "source": [ + "#df_result_merge_trend = df_result_merge\n", + "df_result_merge['qtdtrends'] = np.array(list(tam_username))\n", + "\n", + "ttemp = 0\n", + "iuser = 0\n", + "for user in df_result_merge.tweet_text:\n", + " for trend in trends_unique:\n", + " if user.find(trend) != -1:\n", + " ttemp = ttemp + 1\n", + " print(str(ttemp) + \" - \" + str(iuser) + \" | \" + str((iuser/len(df_result_merge.tweet_text))*100) + \"%\")\n", + " df_result_merge['qtdtrends'][iuser] = ttemp\n", + " iuser = iuser + 1\n", + " ttemp = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dR_UuC-lvEn5" + }, + "outputs": [], + "source": [ + "df_result_merge.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pVg1Ru5vvEn5" + }, + "outputs": [], + "source": [ + "x_novo_trend = x_novo" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c6Mlqz-svEn5" + }, + "source": [ + "Por fim os dados do monitoramento das trendings são incluídos na base de treinamento." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vGip0hkgvEn6" + }, + "outputs": [], + "source": [ + "x_novo_trend['qtdtrends'] = df_result_merge['qtdtrends']\n", + "x_novo_trend['trends_media'] = df_result_merge['trends_media']\n", + "x_novo_trend['trends_max'] = df_result_merge['trends_max']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M0cQ547ivEn6" + }, + "outputs": [], + "source": [ + "x_novo_trend.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jrJgWnrJvEn6" + }, + "source": [ + "**Conjuntos de treinamento e teste**\n", + "\n", + "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r2Ydk5gJvEn6" + }, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xmo-1VWTvEn6" + }, + "outputs": [], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ySGFI_0UvEn6" + }, + "outputs": [], + "source": [ + "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bg7cFJ8VvEn6" + }, + "outputs": [], + "source": [ + "importances = classifier.feature_importances_\n", + "\n", + "indices = np.argsort(importances)\n", + "\n", + "fig, ax = plt.subplots(figsize =(10, 6))\n", + "ax.barh(range(len(importances)), importances[indices])\n", + "ax.set_yticks(range(len(importances)))\n", + "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kN336CjPvEn6" + }, + "source": [ + "**Resultados**\n", + "\n", + "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", + "\n", + "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", + "\n", + "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", + "\n", + "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFWM1W5pvEn6" + }, + "outputs": [], + "source": [ + "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MMZ0DPRDvEn7" + }, + "outputs": [], + "source": [ + "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XPiyVsitvEn7" + }, + "outputs": [], + "source": [ + "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "#classifier = classifier.fit(x_train,y_train)\n", + "#y_pred = classifier.predict(x_test)\n", + "#mean = np.mean(y_pred == y_test)\n", + "#balanced = balanced_accuracy_score(y_test, y_pred)\n", + "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f4DmJ2b6vEn7" + }, + "outputs": [], + "source": [ + "#x_new_trend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ijck93gzvEn7" + }, + "outputs": [], + "source": [ + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OSdmUudLvEn7" + }, + "outputs": [], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jDhWSmiyvEn7" + }, + "outputs": [], + "source": [ + "classifier.predict_proba(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S6sCRdMWvEn7" + }, + "outputs": [], + "source": [ + "predicted_proba = classifier.predict_proba(x_test)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Twh075x1vEn7" + }, + "outputs": [], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HvtvdS0rvEn7" + }, + "outputs": [], + "source": [ + "np.median(classifier.predict_proba(x_test)[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2jorQC_rvEn8" + }, + "outputs": [], + "source": [ + "threshold = 0.6\n", + "predicted = (classifier.predict_proba(x_test)[:,1] >= threshold).astype(bool)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LqsOJwBLvEn8" + }, + "outputs": [], + "source": [ + "np.mean(predicted == y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0M21byuKvEn8" + }, + "outputs": [], + "source": [ + "x_test_geral = x_test\n", + "dtf = [x_test, x_train]\n", + "x_test_geral = pd.concat(dtf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0KZc6CgBvEn8" + }, + "outputs": [], + "source": [ + "print(len(x_test_geral))\n", + "y_test_temp = y_test\n", + "y_test_temp.reset_index(drop=True, inplace=True)\n", + "y_test_temp[y_test_temp == 1].index\n", + "res_geral = classifier.predict_proba(x_test_geral)[y_test_temp.index,1]\n", + "res_sim = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 1].index,1]\n", + "res_nao = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 0].index,1]\n", + "\n", + "np.median(res_sim)\n", + "np.median(res_nao)\n", + "bplots = plt.boxplot([res_geral, res_nao, res_sim], vert = 1, patch_artist = False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GCvfdnSFvEn8" + }, + "outputs": [], + "source": [ + "pd.DataFrame({\"Não\": res_nao}).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_ayLrQFJvEn8" + }, + "outputs": [], + "source": [ + "pd.DataFrame({\"Sim\": res_sim}).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g1biI2dKvEn8" + }, + "source": [ + "**Comparação com as predições do Botometer**\n", + "\n", + "Visando a avaliar a qualidade da classificação dos modelos gerados, os mesmos usuários passaram pela avaliação da ferramenta Botometer, já bem conhecida e amplamente utilizada (apesar de sua aplicação com enfoque nas publicações em Inglês)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NADjnw5qvEn8" + }, + "outputs": [], + "source": [ + "#Lê os dados da aplicação do botometer\n", + "#Busca os dados dos usuários avaliados\n", + "datafile_botometer = \"data/handles_inct.csv\"\n", + "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", + "#Preenche os valores NaN con 0 apenas para avaliação geral\n", + "df_botometer = df_botometer.fillna(0)\n", + "print(len(df_botometer))\n", + "df_botometer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dREze2TlvEn9" + }, + "outputs": [], + "source": [ + "#Avalia os resultados do botometer\n", + "a = len(df_botometer['analise_botometer'])\n", + "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", + "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", + "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", + "botometer_geral = df_botometer['analise_botometer']\n", + "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", + "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DzmZgqDkvEn9" + }, + "outputs": [], + "source": [ + "plt.figure(figsize =(20, 10)) #(11, 6)\n", + "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", + "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", + "c = 0\n", + "for i, bplot in enumerate(bplots['boxes']):\n", + " bplot.set(color=colors[c], linewidth=3)\n", + " c += 1\n", + " \n", + "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", + "c3 = 0\n", + "for cap in bplots['caps']:\n", + " cap.set(color=colorss[c3], linewidth=3)\n", + " c3 +=1\n", + "\n", + "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", + "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", + "plt.ylabel(\"Avaliação do Botometer\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mc5WWVrevEn9" + }, + "outputs": [], + "source": [ + "import scipy\n", + "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H-9ZYAcPvEn9" + }, + "outputs": [], + "source": [ + "scipy.stats.kruskal(res_geral, res_nao,res_sim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LLDuwWGdvEn9" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "colab": { + "provenance": [], + "include_colab_link": true + } }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Carrega as bibliotecas\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.tree import DecisionTreeClassifier \n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.model_selection import train_test_split\n", - "from matplotlib import pyplot as plt\n", - "from sklearn import tree\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef, mean_squared_error, r2_score, mean_absolute_percentage_error, max_error, explained_variance_score, median_absolute_error\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPClassifier, MLPRegressor\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier\n", - "from sklearn.feature_selection import SelectKBest\n", - "from sklearn.feature_selection import chi2\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import CountVectorizer\n", - "from sklearn.feature_extraction.text import TfidfTransformer\n", - "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, classification_report\n", - "import math\n", - "import statistics\n", - "import datetime\n", - "import pytz\n", - "import pickle\n", - "## NLTK (biblioteca para processamento de linguagem natural)\n", - "import nltk\n", - "from nltk.stem.rslp import RSLPStemmer ##http://www.nltk.org/howto/portuguese_en.html\n", - "\n", - "#O primeiro uso exige obter os pacotes adicionais da biblioteca descomentando as linhas a seguir\n", - "#Instala os pacotes de termos do nltk (apenas na primeira vez)\n", - "#nltk.download()\n", - "#nltk.download('rslp')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**O novo modelo de classificação de bots foi construído com base nos usuários manualmente avaliados pelo INCT-DD**\n", - "\n", - "Essa escolha foi tomada considerando que esse conjunto de dados é o melhor que se possui quanto à real possibilidade de um usuário do Twitter ser um bot, não existindo bases de avaliação dentro da realidade brasileira (especialmente quanto ao português), bem como atualizadas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Busca os dados dos usuários avaliados\n", - "datafile_users = \"data/sample2/inct_users.csv\"\n", - "df_users = pd.read_csv(datafile_users, header = 0)\n", - "\n", - "#Preenche os valores NaN con 0 apenas para avaliação geral\n", - "df_users = df_users.fillna(0)\n", - "print(len(df_users))\n", - "#Apresenta o total de usuários avaliados\n", - "df_users.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**No novo modelos são consideradas apenas as informações associadas como \"É bot?\" de respotas \"Sim\" ou \"Não\"**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Busca a classificação do INCT-DD\n", - "datafile_handles = \"data/sample1/handles_inct.csv\" #A classificação é a mesma da sample1\n", - "df_handles = pd.read_csv(datafile_handles, header = 0)\n", - "print(len(df_handles))\n", - "df_handles['É Bot?'].head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**As mais recentes postagens dos usuários foram consideradas como um atributo do modelo**\n", - "\n", - "Para a classificação dos usuários, o novo modelo inclui atributos relacionados com as postagens dos usuários, na tentativa de extrair informação mais atualizada e dinâmica de sua atuação. Entretanto, os textos das postagens foram utilizados unificando seus conteúdos e extraindo informações representativas, tais como os termos mais recorrentemente utilizados, diferença no tempo das postagens e repostagens" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Recupera os últimos twittes\n", - "datafile_timeline = \"data/sample2/inct_timelines.csv\"\n", - "df_timeline = pd.read_csv(datafile_timeline, header = 0)\n", - "print(len(df_timeline))\n", - "df_timeline.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aplica um pré-processamento nos dados para unificar a informação da postagens se tratar de um retweet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#identifica os formatos existentes\n", - "df_timeline['tweet_is_retweet'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_timeline['retweet_tratado'] = df_timeline['tweet_is_retweet'].apply(lambda x: \"sim\" if (x == 'True' or x == True) else \"não\")\n", - "df_timeline['retweet_tratado'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Necessário reverificar no texto do tweet por RT @, pois o campo tweet_is_retweet falha em algumas situações não identificadas\n", - "#Parecem ser os RT com comentários adicionais\n", - "#for tweet in df_timeline['retweet_tratado', 'tweet_text']:\n", - "# if tweet['retweet_tratado'] == 'não':\n", - "# if tweet['tweet_text'].find(\"RT @\") != -1:\n", - "# tweet['retweet_tratado'] = 'sim'\n", - "#len(df_timeline)\n", - "#for i in range(len(df_timeline)):\n", - "# if df_timeline.iloc[i]['retweet_tratado'] == 'não':\n", - "# if df_timeline.iloc[i]['tweet_text'].find(\"RT @\") != -1:\n", - "# df_timeline.iloc[i]['retweet_tratado'] = 'sim'\n", - "df_timeline['tweet_com_rt_tratado'] = df_timeline['tweet_text'].apply(lambda x: \"sim\" if x.find(\"RT @\") != -1 else \"não\" )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Combina em uma única coluna as informações de retweets e tweets com RT comentados\n", - "def reune_rt(retweet,rt):\n", - " if retweet == 'sim' or rt == 'sim':\n", - " return 'sim'\n", - " else:\n", - " return 'não'\n", - "\n", - "df_timeline['retweet_e_tweet_com_rt_tratado'] = df_timeline.apply(lambda x: reune_rt(x.retweet_tratado, x.tweet_com_rt_tratado), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_timeline[df_timeline[\"retweet_e_tweet_com_rt_tratado\"] == 'sim']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extrai a diferença em segundos entre as postagens do usuário" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Incluir uma dedida da distancia temporal entre twittes (mediana e mínimo)\n", - "df_handles['Tempo mediano'] = np.array(len(df_handles))\n", - "df_handles['Tempo menor'] = np.array(len(df_handles))\n", - "iuser = 0\n", - "for user in df_handles['handle']:\n", - " df_temp = df_timeline[df_timeline['tweet_author'] == user]\n", - " itweet = 0\n", - " menor = 100000\n", - " difs = list()\n", - " tweet_date_prev = None\n", - " for tweet in df_temp['tweet_created_at']:\n", - " tweet_date = pd.to_datetime(pd.to_datetime(tweet).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"))\n", - " if itweet > 0:\n", - " dif = (tweet_date_prev - tweet_date).seconds\n", - " if dif < menor:\n", - " menor = dif\n", - " difs.append(dif)\n", - " else:\n", - " tweet_date_prev = tweet_date\n", - " tweet_date_prev = tweet_date\n", - " itweet += 1\n", - " if len(difs) > 0:\n", - " mediana = statistics.median(difs)\n", - " else:\n", - " mediana = 1000\n", - " print(user + ' - ' + str(menor) + ' - ' + str(mediana)+'\\n')\n", - " df_handles['Tempo mediano'][iuser] = mediana\n", - " df_handles['Tempo menor'][iuser] = menor\n", - " iuser += 1\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Os dados inicialmente tratados são reunidos com a classificação dada pelo INCT-DD**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune os dados do usuário com a classificação\n", - "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", - "print(len(df_result_merge))\n", - "df_result_merge.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Os dados das postagens foram reunidos para a extração de informações representativas**\n", - "\n", - "Para viabilizar o treinamento do modelo, os dados por postagens foram convertidos em conjuntos por usuário (autor do tweet, e a representação foi dada por informações sumarizadas ou probabilísticas, por exemplo, as hashtags mais utilizadas ou o percentual de postagens realizadas a partir do Android, iPhone ou Web." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune todos os tweets de um mesmo autor em um único texto, separando apenas por vírgula\n", - "df_result_text = df_timeline.groupby('tweet_author').agg({'tweet_text':lambda col: ', '.join(col)}).reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune todos as hashtags utilizadas por um mesmo autor em um único texto, separando apenas por vírgula\n", - "df_result_hashtags = df_timeline.groupby('tweet_author').agg({'tweet_hashtags':lambda col: ', '.join(col)}).reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune a informação de fonte de todos os tweets de um mesmo autor em um único texto, separando apenas por vírgula\n", - "df_result_source = df_timeline.groupby('tweet_author').agg({'tweet_source':lambda col: ', '.join(col)}).reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune as informações de twettes que são retweets\n", - "df_result_retweet = df_timeline.groupby('tweet_author').agg({'retweet_tratado':lambda col: ', '.join(col)}).reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune as informações de twettes com RT\n", - "df_result_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", - "df_result_tweet_com_rt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune as informações da junção de retweets e tweets com rt\n", - "df_result_retweet_e_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'retweet_e_tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", - "df_result_retweet_e_tweet_com_rt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune os dados (merge) do usuários, suas avaliações com texto dos tweets, as hashtags, as fontes e os retweets\n", - "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_hashtags, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_source, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_retweet, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_retweet_e_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Exibe parte dos resultados da junção (nem todos os usuários ainda estão ativos e número de amostras diminui)\n", - "print(len(df_result_merge))\n", - "df_result_merge.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A classificação dos usuários foi padronizada para 0 - Não Bot e 1 - Bot**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", - "df = df_result_merge\n", - "y = df['É Bot?'].apply(lambda x: 1 if (x == 'Sim' or x == 'sim') else 0)\n", - "y.reset_index(drop=True, inplace=True)\n", - "y.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Seleciona as colunas para o conjunto X\n", - "#feature_cols = ['tweet_text'] #,'tweet_source','tweet_hashtags'\n", - "#x = df['tweet_text']\n", - "#x.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "** [Classficando apenas pelo texto dos Twittes (NLTK)] **" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Prepara o conjunto de dados para treinamento e teste\n", - "#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Método para vetorizar e contabilizar os termos\n", - "stemmer = nltk.stem.RSLPStemmer()\n", - "class StemmedCountVectorizerRSLPS(CountVectorizer):\n", - " def build_analyzer(self):\n", - " analyzer = super(StemmedCountVectorizerRSLPS, self).build_analyzer()\n", - " return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n", - "stemmed_count_vect = StemmedCountVectorizerRSLPS(stop_words=nltk.corpus.stopwords.words('portuguese'))\n", - "tfidf_transformer = TfidfTransformer()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Pipeline para extrair as informaçoes e classificar com base no texto (pode ser usado ANN ou MNB [MultinomialNB(fit_prior=False)])\n", - "#text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),\n", - "# ('tfidf', TfidfTransformer()),\n", - "# ('mnb', MLPClassifier(random_state=1, max_iter=600, activation='relu',solver='adam')),\n", - "#])\n", - "#text_mnb_stemmed = text_mnb_stemmed.fit(x_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#text_mnb_stemmed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Avalia a classificação\n", - "#predicted_mnb_stemmed = text_mnb_stemmed.predict(x_test)\n", - "#np.mean(predicted_mnb_stemmed == y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Os atributos do treinamentos envolvem diversos fatores**\n", - "\n", - "Uma das etapas mais critícas da modelagem é a definição dos atributos que representam o cenário real, nesse sentido foram incluídas o máximo de variáveis que pudessem representar um usuário e suas atividades na rede, desde o tamanho do login escolhido até o tempo mínimo entre suas postagens. Na sequência são realizadas as atividades de extração, tratamento e junção dessas informações como atributos do conjunto de treinamento do modelo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.columns #df é o conjunto completo de dados, já com os twittes-hashtags-sources-retweets em campos únicos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "De todo os conjuntos de informações disponíveis não foram selecionados aquelas que não poderiam ser automaticamente extraídos dos perfis e atividades dos usuários na rede. Portanto, as classificações como \"comportamento agressivo?\", \"Parece só Retweetar?\", entre outras, não foram incluídos no conjunto de treinamento." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "feature_cols = ['followers_count', 'friends_count', 'Tempo mediano', 'Tempo menor']\n", - "x = df[feature_cols]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Converte os testos em frequências\n", - "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", - "#tfidf_transformer = TfidfTransformer()\n", - "#x_tfidf = tfidf_transformer.fit_transform(st)\n", - "#x_tfidf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Inclui as frequências no conjunto x\n", - "#x_tfidf.shape\n", - "#x.join(pd.DataFrame(x_tfidf.todense()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(df['tweet_hashtags'][7].replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\"))\n", - "len(df['tweet_hashtags'][7].split(\", [\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Inclui os quantitativos de hashtages utilizadas (e a mediana por postagem)\n", - "\n", - "qtd_hashtags = df['tweet_hashtags'].apply(lambda x: len(x.replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\")))\n", - "x['Quantidade hashtags'] = np.array(list(qtd_hashtags))\n", - "qtd_hashtags_media = df['tweet_hashtags'].apply(lambda x: len(x.replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\"))/len(x.split(\", [\")))\n", - "x['Quantidade hashtags media'] = np.array(list(qtd_hashtags_media))\n", - "\n", - "x.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Inclui o número de dígitos no nome\n", - "username_digitos = df['handle'].apply(lambda x: sum(c.isdigit() for c in str(x)) ) \n", - "x['Digitos no username'] = np.array(list(username_digitos))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#O tamanho do nome e do login\n", - "tam_username = df['handle'].apply(lambda x: len(str(x)))\n", - "tam_nome = df['name'].apply(lambda x: len(str(x)))\n", - "x['Tamanho do username'] = np.array(list(tam_username))\n", - "x['Tamanho do nome'] = np.array(list(tam_nome))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A fonte do tweet foi considera importante informação, considerando que automações de postagens possam ser facilitadas a partir da versão Web ou que possa existir algum padrão no uso das diferentes fontes. Sendo assim, forneceu-se ao métodos a informação percentual da origem das postagens do mesmo usuário, seja Android, iPhone ou Web." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Calcula a quantidade de twittes por fontes\n", - "fonte_android = df['tweet_source'].apply(lambda x: str(x).count('Twitter for Android') )\n", - "fonte_iphone = df['tweet_source'].apply(lambda x: str(x).count('Twitter for iPhone') )\n", - "fonte_web = df['tweet_source'].apply(lambda x: str(x).count('Twitter Web App') )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fonte_soma = fonte_android + fonte_iphone + fonte_web\n", - "fonte_soma = fonte_soma.apply(lambda x: 1 if x <= 0 else x )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Calcula o percentual por usuário\n", - "fonte_android = fonte_android/fonte_soma\n", - "fonte_iphone = fonte_iphone/fonte_soma\n", - "fonte_web = fonte_web/fonte_soma" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x['Fonte de Android'] = np.array(list(fonte_android))\n", - "x['Fonte de iPhone'] = np.array(list(fonte_iphone))\n", - "x['Fonte de Web'] = np.array(list(fonte_web))\n", - "x = x.fillna(0)\n", - "x.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Avaliação geral das diferentes fontes\n", - "x['Fonte de Android'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x['Fonte de iPhone'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x['Fonte de Web'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Inclui a informação do retweet\n", - "df['retweet_tratado'].head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "retweet_tratado = df['retweet_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['retweet_tratado_media'] = np.array(list(retweet_tratado))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tweet_com_rt = df['tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['tweet_com_rt_tratado_media'] = np.array(list(tweet_com_rt))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "retweet_e_tweet_com_rt = df['retweet_e_tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['retweet_e_tweet_com_rt_tratado_media'] = np.array(list(retweet_e_tweet_com_rt))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo = x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Inclui os textos dos twittes (NLTK)\n", - "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", - "#tfidf_transformer = TfidfTransformer()\n", - "#x_tfidf = tfidf_transformer.fit_transform(st)\n", - "#x_tfidf\n", - "#x_novo = x.join(pd.DataFrame(x_tfidf.todense()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Com o primeiro conjunto de atributos formado é possível separar o conjunto de dados em treinamento e teste para a elaboração do modelo**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Cria um modelo de classificação para o conjunto completo\n", - "x_train, x_test, y_train, y_test = train_test_split(x_novo, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "np.mean(y_pred == y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "##Seleciona os atributos mais \"importantes\"\n", - "#x_new = SelectKBest(chi2, k=20).fit_transform(x_novo, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Classificação com RNA\n", - "classifier = MLPClassifier(max_iter=1200, random_state=1, activation='tanh', solver='adam') #activation: logistic, relu, tanh, identity | solver: lbfgs, sgd, adam\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Informações de trend topics**\n", - "\n", - "Outra informação que se mostrou de relevância ao longo do trabalho de modelagem foi a relação das postagens de bots com as menções e hashtags listadas nos mais atuais 'trend topics', ou seja, o aparente uso de termos altamente utilizados no momento para possivelmente alavancar a visibilidade da postagem.\n", - "\n", - "Para averiguar essa possibilidade, um sistema de monitoramento dos tópicos mais mencionados foi criado e cada postagem coletada do usuário foi confrontado com os 'trend topics' do período mais próximo. Esse confrontamento gerou um percentual de uso desses tópicos nas postagens dos usuários." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Busca os dados de todas as trending topics recuperadas\n", - "datafile_trends = \"data/sample2/trends_dataclips_qijpjdyxutqsnrteglrjtwjhdjja.csv\"\n", - "df_trends = pd.read_csv(datafile_trends, header = 0)\n", - "#Preenche os valores NaN con 0 apenas para avaliação geral\n", - "df_trends = df_trends.fillna(0)\n", - "print(len(df_trends))\n", - "df_trends.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entre os passos de tratamentos dos dados das \"trend topics\" está o ajuste dos padrões de data e hora dos registros, tanto dos tópicos monitorados quanto dos próprios tweets.\n", - "A seguir são extraídas as datas dos tweets no formato yyyy-mm-dd, dentro da conversão nos próximos trechos foi também necessário ajustar o \"timezone\" desses dados." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Inclui um percentual de trending topics utilizado por tweet\n", - "#Para tweet, busca pelos trending topics imediatamente anteriores\n", - "df_timeline['Numero de trendings'] = np.array(len(df_timeline))\n", - "df_timeline['Numero de trendings'] = 0\n", - "df_trends['Trend Date Time Convertido'] = np.array(len(df_trends))\n", - "\n", - "itrend = 0\n", - "for x in df_trends['trend_date_time']:\n", - " df_trends['Trend Date Time Convertido'][itrend] = pd.to_datetime(x).strftime(\"%Y-%m-%d\")\n", - " itrend += 1\n", - "\n", - "df_trends.head() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "O relacionamento dos trends e dos tweets foi realizado percorrendo todos os trends armazenados para cada tweet em data anterior ao do tweet e, para cada trend nessa condição, verificou-se no texto do tweet a presença de trendings. Caso esteja presente acumulou-se essa ocorrência, finalizando com a ocorrência de uso de uma trend por cada tweet.\n", - "Este trecho demanda de melhorias em desempenho e na inclusão de restrições que reduzam o tempo de ocorrência da trend para mais próximo do tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "itweet = 0\n", - "for tweet in df_timeline['tweet_created_at']:\n", - " tweet_date = pd.to_datetime(pd.to_datetime(tweet).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"))\n", - " df_temp = df_trends[df_trends['Trend Date Time Convertido'] == tweet_date.strftime(\"%Y-%m-%d\")] \n", - " \n", - " itrend = 0\n", - " for trend in df_temp['Trend Date Time Convertido']:\n", - " trend_date = pd.to_datetime(pd.to_datetime(trend).strftime(\"%Y-%m-%d\"))\n", - " if trend_date <= tweet_date.tz_convert(None):\n", - " if df_timeline['tweet_text'][itweet].find(df_trends['trend'][itrend]) != -1: \n", - " df_timeline['Numero de trendings'][itweet] = df_timeline['Numero de trendings'][itweet] + 1\n", - " itrend += 1\n", - " print(itweet) \n", - " itweet += 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Para cada tweet foi armazenados o número de trend topics encontrado." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_timeline[df_timeline['Numero de trendings'] > 0].describe()\n", - "df_timeline['Numero de trendings'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_timeline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As quantidades de trendings utilizadas em cada tweet foram agrupados por autor (usuário), assim foram incluídos na base de treinamento o número de trendings utilizadas, a média de trendings por tweet desse autor e o número máximo de trendings usado em um mesmo tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Reune as informações de trends nos tweets por author\n", - "df_result_trend = df_timeline.groupby('tweet_author').agg({'Numero de trendings':lambda col: sum(col)/len(col)}).reset_index()\n", - "df_result_trend_max = df_timeline.groupby('tweet_author').agg({'Numero de trendings':lambda col: max(col)}).reset_index()\n", - "df_result_trend['trends_media'] = df_result_trend['Numero de trendings']\n", - "df_result_trend_max['trends_max'] = df_result_trend_max['Numero de trendings']\n", - "df_result_trend_max" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_handles.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_trends.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trends_unique = df_trends.trend.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_result_merge.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Os valores referentes aos trendings do usuário são reunidos (\"merged\") com os dados gerais do usuário" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_result_merge = pd.merge(df_result_merge,df_result_trend, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_trend_max, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#df_result_merge_trend = df_result_merge\n", - "df_result_merge['qtdtrends'] = np.array(list(tam_username))\n", - "\n", - "ttemp = 0\n", - "iuser = 0\n", - "for user in df_result_merge.tweet_text:\n", - " for trend in trends_unique:\n", - " if user.find(trend) != -1:\n", - " ttemp = ttemp + 1\n", - " print(str(ttemp) + \" - \" + str(iuser) + \" | \" + str((iuser/len(df_result_merge.tweet_text))*100) + \"%\")\n", - " df_result_merge['qtdtrends'][iuser] = ttemp\n", - " iuser = iuser + 1\n", - " ttemp = 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_result_merge.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo_trend = x_novo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Por fim os dados do monitoramento das trendings são incluídos na base de treinamento." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo_trend['qtdtrends'] = df_result_merge['qtdtrends']\n", - "x_novo_trend['trends_media'] = df_result_merge['trends_media']\n", - "x_novo_trend['trends_max'] = df_result_merge['trends_max']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_novo_trend.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Conjuntos de treinamento e teste**\n", - "\n", - "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "importances = classifier.feature_importances_\n", - "\n", - "indices = np.argsort(importances)\n", - "\n", - "fig, ax = plt.subplots(figsize =(10, 6))\n", - "ax.barh(range(len(importances)), importances[indices])\n", - "ax.set_yticks(range(len(importances)))\n", - "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Resultados**\n", - "\n", - "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", - "\n", - "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", - "\n", - "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", - "\n", - "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "#classifier = classifier.fit(x_train,y_train)\n", - "#y_pred = classifier.predict(x_test)\n", - "#mean = np.mean(y_pred == y_test)\n", - "#balanced = balanced_accuracy_score(y_test, y_pred)\n", - "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "#confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#x_new_trend" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifier.predict_proba(x_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predicted_proba = classifier.predict_proba(x_test)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.median(classifier.predict_proba(x_test)[:,1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "threshold = 0.6\n", - "predicted = (classifier.predict_proba(x_test)[:,1] >= threshold).astype(bool)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.mean(predicted == y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_test_geral = x_test\n", - "dtf = [x_test, x_train]\n", - "x_test_geral = pd.concat(dtf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(len(x_test_geral))\n", - "y_test_temp = y_test\n", - "y_test_temp.reset_index(drop=True, inplace=True)\n", - "y_test_temp[y_test_temp == 1].index\n", - "res_geral = classifier.predict_proba(x_test_geral)[y_test_temp.index,1]\n", - "res_sim = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 1].index,1]\n", - "res_nao = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 0].index,1]\n", - "\n", - "np.median(res_sim)\n", - "np.median(res_nao)\n", - "bplots = plt.boxplot([res_geral, res_nao, res_sim], vert = 1, patch_artist = False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame({\"Não\": res_nao}).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame({\"Sim\": res_sim}).describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Comparação com as predições do Botometer**\n", - "\n", - "Visando a avaliar a qualidade da classificação dos modelos gerados, os mesmos usuários passaram pela avaliação da ferramenta Botometer, já bem conhecida e amplamente utilizada (apesar de sua aplicação com enfoque nas publicações em Inglês)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Lê os dados da aplicação do botometer\n", - "#Busca os dados dos usuários avaliados\n", - "datafile_botometer = \"data/handles_inct.csv\"\n", - "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", - "#Preenche os valores NaN con 0 apenas para avaliação geral\n", - "df_botometer = df_botometer.fillna(0)\n", - "print(len(df_botometer))\n", - "df_botometer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Avalia os resultados do botometer\n", - "a = len(df_botometer['analise_botometer'])\n", - "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", - "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", - "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", - "botometer_geral = df_botometer['analise_botometer']\n", - "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", - "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize =(20, 10)) #(11, 6)\n", - "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", - "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", - "c = 0\n", - "for i, bplot in enumerate(bplots['boxes']):\n", - " bplot.set(color=colors[c], linewidth=3)\n", - " c += 1\n", - " \n", - "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", - "c3 = 0\n", - "for cap in bplots['caps']:\n", - " cap.set(color=colorss[c3], linewidth=3)\n", - " c3 +=1\n", - "\n", - "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", - "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", - "plt.ylabel(\"Avaliação do Botometer\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import scipy\n", - "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "scipy.stats.kruskal(res_geral, res_nao,res_sim)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 5672057e756c28fabeaa5a17b71e36b327b7408c Mon Sep 17 00:00:00 2001 From: Carla Oliveira Date: Sat, 3 Sep 2022 21:53:43 -0300 Subject: [PATCH 2/9] Criado usando o Colaboratory --- New-Model-From-INCT-DD-Evaluation.ipynb | 12876 +++++++++++----------- 1 file changed, 6462 insertions(+), 6414 deletions(-) diff --git a/New-Model-From-INCT-DD-Evaluation.ipynb b/New-Model-From-INCT-DD-Evaluation.ipynb index 1a2a93c..4526da2 100644 --- a/New-Model-From-INCT-DD-Evaluation.ipynb +++ b/New-Model-From-INCT-DD-Evaluation.ipynb @@ -21,11 +21,26 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 107, "metadata": { - "id": "pfW-ynZ3vEne" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pfW-ynZ3vEne", + "outputId": "559ac919-ea54-4767-ece6-e11ca6501b4c" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package rslp to /root/nltk_data...\n", + "[nltk_data] Package rslp is already up-to-date!\n", + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + } + ], "source": [ "#Carrega as bibliotecas\n", "import pandas as pd\n", @@ -53,7 +68,10 @@ "import pickle\n", "## NLTK (biblioteca para processamento de linguagem natural)\n", "import nltk\n", + "nltk.download('rslp')\n", + "nltk.download('stopwords')\n", "from nltk.stem.rslp import RSLPStemmer ##http://www.nltk.org/howto/portuguese_en.html\n", + "from nltk.corpus import stopwords\n", "\n", "#O primeiro uso exige obter os pacotes adicionais da biblioteca descomentando as linhas a seguir\n", "#Instala os pacotes de termos do nltk (apenas na primeira vez)\n", @@ -74,14 +92,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 109, "metadata": { - "id": "OyGwd_QQvEnh", - "outputId": "85898ffb-7dfe-4438-f556-e65ef8979cbe", "colab": { "base_uri": "https://localhost:8080/", "height": 461 - } + }, + "id": "OyGwd_QQvEnh", + "outputId": "a53ff9b4-5bb5-4f5a-a802-8b455b92420e" }, "outputs": [ { @@ -139,7 +157,7 @@ ], "text/html": [ "\n", - "
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtagstweet_idtweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_text
00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]1.501380e+1815013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 145 + } + ], + "source": [ + "#Recupera os últimos twittes\n", + "datafile_timeline = \"/content/sample_data/inct_timelines.csv\"\n", + "df_timeline = pd.read_csv(datafile_timeline, header = 0)\n", + "print(len(df_timeline))\n", + "df_timeline.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BnnbMc0jvEnm" + }, + "source": [ + "Aplica um pré-processamento nos dados para unificar a informação da postagens se tratar de um retweet" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MxTnMw6evEnm", + "outputId": "cd379da3-7627-4418-cd8e-a52996f1f0e2" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { "text/plain": [ "array(['0.0', 'False', 'True', False, True], dtype=object)" ] }, "metadata": {}, - "execution_count": 6 + "execution_count": 117 } ], "source": [ @@ -777,13 +980,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 122, "metadata": { - "id": "6kAf3fAFvEnn", - "outputId": "94a6ea94-910f-48ce-d448-daefe8506ffd", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "6kAf3fAFvEnn", + "outputId": "767a7aff-8154-4242-9f59-66d4a69a9d11" }, "outputs": [ { @@ -794,7 +997,7 @@ ] }, "metadata": {}, - "execution_count": 7 + "execution_count": 122 } ], "source": [ @@ -804,11 +1007,37 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 123, "metadata": { - "id": "KRPYHx4-vEnn" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KRPYHx4-vEnn", + "outputId": "6fffd410-a13f-4e75-dca8-068d96b70b67" }, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 não\n", + "1 sim\n", + "2 não\n", + "3 não\n", + "4 não\n", + " ... \n", + "82408 sim\n", + "82409 sim\n", + "82410 sim\n", + "82411 sim\n", + "82412 não\n", + "Name: tweet_com_rt_tratado, Length: 82413, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 123 + } + ], "source": [ "#Necessário reverificar no texto do tweet por RT @, pois o campo tweet_is_retweet falha em algumas situações não identificadas\n", "#Parecem ser os RT com comentários adicionais\n", @@ -821,19 +1050,20 @@ "# if df_timeline.iloc[i]['retweet_tratado'] == 'não':\n", "# if df_timeline.iloc[i]['tweet_text'].find(\"RT @\") != -1:\n", "# df_timeline.iloc[i]['retweet_tratado'] = 'sim'\n", - "df_timeline['tweet_com_rt_tratado'] = df_timeline['tweet_text'].apply(lambda x: \"sim\" if x.find(\"RT @\") != -1 else \"não\" )" + "df_timeline['tweet_com_rt_tratado'] = df_timeline['tweet_text'].apply(lambda x: \"sim\" if x.find(\"RT @\") != -1 else \"não\" )\n", + "df_timeline['tweet_com_rt_tratado']" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 125, "metadata": { - "id": "L7lkmE_yvEno", - "outputId": "25f170c1-c6f0-4104-a638-7caf136219a5", "colab": { "base_uri": "https://localhost:8080/", - "height": 560 - } + "height": 310 + }, + "id": "L7lkmE_yvEno", + "outputId": "da8a6297-b9d5-4734-8330-4a139497c497" }, "outputs": [ { @@ -843,50 +1073,32 @@ " Unnamed: 0 error tweet_author tweet_author_id_str tweet_contributors \\\n", "0 0 NaN lemathes 52253248 NaN \n", "1 1 NaN lemathes 52253248 NaN \n", - "2 2 NaN lemathes 52253248 NaN \n", - "3 3 NaN lemathes 52253248 NaN \n", - "4 4 NaN lemathes 52253248 NaN \n", "\n", " tweet_created_at tweet_favorite_count tweet_favorited tweet_geo \\\n", "0 2022-03-09 02:10:58+00:00 0.0 0.0 NaN \n", "1 2022-03-09 02:10:12+00:00 0.0 False NaN \n", - "2 2022-03-02 21:57:17+00:00 0.0 False NaN \n", - "3 2022-03-02 16:57:51+00:00 1.0 False NaN \n", - "4 2022-03-02 16:54:56+00:00 0.0 False NaN \n", "\n", " tweet_hashtags ... tweet_id_str tweet_is_retweet tweet_lang \\\n", "0 [] ... 1501379987747876874 0.0 pt \n", "1 [] ... 1501379796210757632 False pt \n", - "2 [] ... 1499141820722421760 False pt \n", - "3 [] ... 1499066467916079105 False pt \n", - "4 [] ... 1499065733086695425 False pt \n", "\n", " tweet_place tweet_retweeted tweet_source \\\n", "0 NaN 0.0 Twitter for Android \n", "1 NaN False Twitter for Android \n", - "2 NaN False Twitter for Android \n", - "3 NaN False Twitter for Android \n", - "4 NaN False Twitter for Android \n", - "\n", - " tweet_text retweet_tratado \\\n", - "0 @LucianoHangBr Já demorou muito! não \n", - "1 RT @LucianoHangBr: A vida precisa continuar e ... não \n", - "2 Pq ñ mandam uma bomba na cabeça do Pudim e aca... não \n", - "3 @carteiroreaca Usa máscara, quem quer e acha q... não \n", - "4 @carteiroreaca Isso aí!!! 👏👏👏👏 Já demorou de m... não \n", - "\n", - " tweet_com_rt_tratado retweet_e_tweet_com_rt_tratado \n", - "0 não não \n", - "1 sim sim \n", - "2 não não \n", - "3 não não \n", - "4 não não \n", - "\n", - "[5 rows x 21 columns]" + "\n", + " tweet_text tweet_com_rt_tratado \\\n", + "0 @LucianoHangBr Já demorou muito! não \n", + "1 RT @LucianoHangBr: A vida precisa continuar e ... sim \n", + "\n", + " retweet_tratado retweet_e_tweet_com_rt_tratado \n", + "0 não não \n", + "1 não sim \n", + "\n", + "[2 rows x 21 columns]" ], "text/html": [ "\n", - "
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tweet_authortweet_hashtags
0100_bolsonaro['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...
113valber1[], [], [], [], [], [], [], [], [], [], [], []...
21976Mnc[], [], [], [], [], [], ['PLP235NÃO'], [], ['P...
3ACamargo241[], [], [], [], [], [], [], [], [], [], [], []...
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNlemathes191716
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNMaurcio98905595221
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNLunViana342
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonãoNaNfelipeleixas40791141
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNJoseCar414511945849
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342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes0.000.00lemathes00
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio9890559500
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...LunViana0.010.01LunViana11
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas00
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar414511940.000.00JoseCar4145119400
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82910661067https://twitter.com/@CesarNi859393841111.csvSimRetweetarNaNnãosimsim...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi859393840.000.00CesarNi8593938400
83010681069https://twitter.com/@PauloRo491953611111.csvSimRetweetarNaNnãosimsim...PauloRo49195361não, sim, sim, sim, não, sim, sim, sim, sim, s...PauloRo49195361não, sim, sim, sim, não, sim, sim, sim, sim, s...PauloRo491953610.000.00PauloRo4919536100
83110701071https://twitter.com/@Marina920119591111.csvSimRetweetarNaNnãonãosim...Marina92011959não, não, não, não, não, sim, não, não, não, n...Marina92011959não, não, não, não, não, sim, não, não, não, n...Marina920119590.000.00Marina9201195900
83210711072https://twitter.com/@Marcos_28_11_661111.csvSimRetweetarNaNnãonãonão...Marcos_28_11_66sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Marcos_28_11_66sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Marcos_28_11_660.000.00Marcos_28_11_6600
83310731074https://twitter.com/@FATIMAC758431781111.csvSimRetweetarNaNnãosimsim...FATIMAC75843178não, sim, sim, não, não, não, não, não, não, n...FATIMAC75843178não, sim, sim, não, não, não, não, não, não, n...FATIMAC758431780.000.00FATIMAC7584317800
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Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não não se aplica NaN \n", + "1 não não se aplica NaN \n", + "2 não não se aplica NaN \n", + "3 sim Publicar hashtags Atacar \n", + "4 Não não se aplica NaN \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 sim sim \n", + "4 não não \n", + "\n", + " Parece só Retweetar? ... \\\n", + "0 não ... \n", + "1 não ... \n", + "2 não ... \n", + "3 não ... \n", + "4 não ... \n", + "\n", + " tweet_com_rt_tratado tweet_author_y \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "1 não, não, sim, não, não, sim, não, sim, não, n... Maurcio98905595 \n", + "2 sim, sim, sim, sim, não, sim, sim, sim, sim, s... LunViana \n", + "3 não, não, não, não, sim, não, não, não, não, n... felipeleixas \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... JoseCar41451194 \n", + "\n", + " retweet_e_tweet_com_rt_tratado tweet_author_x \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "1 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... Maurcio98905595 \n", + "2 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... LunViana \n", + "3 não, não, não, não, sim, não, não, não, não, n... felipeleixas \n", + "4 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... JoseCar41451194 \n", + "\n", + " Numero de trendings_x trends_media tweet_author_y Numero de trendings_y \\\n", + "0 0.00 0.00 lemathes 0 \n", + "1 0.00 0.00 Maurcio98905595 0 \n", + "2 0.01 0.01 LunViana 1 \n", + "3 0.00 0.00 felipeleixas 0 \n", + "4 0.00 0.00 JoseCar41451194 0 \n", + "\n", + " trends_max qtdtrends \n", + "0 0 8 \n", + "1 0 15 \n", + "2 1 8 \n", + "3 0 12 \n", + "4 0 15 \n", + "\n", + "[5 rows x 53 columns]" + ], + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratadotweet_author_xNumero de trendings_xtrends_mediatweet_author_yNumero de trendings_ytrends_maxqtdtrends
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...não, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes0.000.00lemathes008
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio989055950015
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334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...não, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas0012
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\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 102 + } + ], + "source": [ + "x_novo_trend.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jrJgWnrJvEn6" + }, + "source": [ + "**Conjuntos de treinamento e teste**\n", + "\n", + "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "id": "r2Ydk5gJvEn6" + }, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xmo-1VWTvEn6", + "outputId": "2d7ed19b-8c7e-4df6-9c3d-0f2dff72105c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: 0.749003984063745 | Balanced accuracy: 0.7019175933846593\n", + "Score: 0.749003984063745\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 47, 37],\n", + " [ 26, 141]])" + ] + }, + "metadata": {}, + "execution_count": 104 + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ySGFI_0UvEn6", + "outputId": "9b97db9b-f283-451d-c554-761c46054a6f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: 0.7569721115537849 | Balanced accuracy: 0.704947248360422\n", + "Score: 0.7569721115537849\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 46, 38],\n", + " [ 23, 144]])" + ] + }, + "metadata": {}, + "execution_count": 105 + } + ], + "source": [ + "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "bg7cFJ8VvEn6", + "outputId": "33f8e557-fa66-4d09-9b91-db439b4ac4b0" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "importances = classifier.feature_importances_\n", + "\n", + "indices = np.argsort(importances)\n", + "\n", + "fig, ax = plt.subplots(figsize =(10, 6))\n", + "ax.barh(range(len(importances)), importances[indices])\n", + "ax.set_yticks(range(len(importances)))\n", + "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kN336CjPvEn6" + }, + "source": [ + "**Resultados**\n", + "\n", + "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", + "\n", + "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", + "\n", + "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", + "\n", + "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFWM1W5pvEn6" + }, + "outputs": [], + "source": [ + "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MMZ0DPRDvEn7" + }, + "outputs": [], + "source": [ + "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XPiyVsitvEn7" + }, + "outputs": [], + "source": [ + "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "#classifier = classifier.fit(x_train,y_train)\n", + "#y_pred = classifier.predict(x_test)\n", + "#mean = np.mean(y_pred == y_test)\n", + "#balanced = balanced_accuracy_score(y_test, y_pred)\n", + "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f4DmJ2b6vEn7" + }, + "outputs": [], + "source": [ + "#x_new_trend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ijck93gzvEn7" + }, + "outputs": [], + "source": [ + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OSdmUudLvEn7" + }, + "outputs": [], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jDhWSmiyvEn7" + }, + "outputs": [], + "source": [ + "classifier.predict_proba(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "S6sCRdMWvEn7" + }, + "outputs": [], + "source": [ + "predicted_proba = classifier.predict_proba(x_test)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Twh075x1vEn7" + }, + "outputs": [], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HvtvdS0rvEn7" + }, + "outputs": [], + "source": [ + "np.median(classifier.predict_proba(x_test)[:,1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2jorQC_rvEn8" + }, + "outputs": [], + "source": [ + "threshold = 0.6\n", + "predicted = (classifier.predict_proba(x_test)[:,1] >= threshold).astype(bool)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LqsOJwBLvEn8" + }, + "outputs": [], + "source": [ + "np.mean(predicted == y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0M21byuKvEn8" + }, + "outputs": [], + "source": [ + "x_test_geral = x_test\n", + "dtf = [x_test, x_train]\n", + "x_test_geral = pd.concat(dtf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0KZc6CgBvEn8" + }, + "outputs": [], + "source": [ + "print(len(x_test_geral))\n", + "y_test_temp = y_test\n", + "y_test_temp.reset_index(drop=True, inplace=True)\n", + "y_test_temp[y_test_temp == 1].index\n", + "res_geral = classifier.predict_proba(x_test_geral)[y_test_temp.index,1]\n", + "res_sim = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 1].index,1]\n", + "res_nao = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 0].index,1]\n", + "\n", + "np.median(res_sim)\n", + "np.median(res_nao)\n", + "bplots = plt.boxplot([res_geral, res_nao, res_sim], vert = 1, patch_artist = False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GCvfdnSFvEn8" + }, + "outputs": [], + "source": [ + "pd.DataFrame({\"Não\": res_nao}).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_ayLrQFJvEn8" + }, + "outputs": [], + "source": [ + "pd.DataFrame({\"Sim\": res_sim}).describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g1biI2dKvEn8" + }, + "source": [ + "**Comparação com as predições do Botometer**\n", + "\n", + "Visando a avaliar a qualidade da classificação dos modelos gerados, os mesmos usuários passaram pela avaliação da ferramenta Botometer, já bem conhecida e amplamente utilizada (apesar de sua aplicação com enfoque nas publicações em Inglês)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NADjnw5qvEn8" + }, + "outputs": [], + "source": [ + "#Lê os dados da aplicação do botometer\n", + "#Busca os dados dos usuários avaliados\n", + "datafile_botometer = \"data/handles_inct.csv\"\n", + "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", + "#Preenche os valores NaN con 0 apenas para avaliação geral\n", + "df_botometer = df_botometer.fillna(0)\n", + "print(len(df_botometer))\n", + "df_botometer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dREze2TlvEn9" + }, + "outputs": [], + "source": [ + "#Avalia os resultados do botometer\n", + "a = len(df_botometer['analise_botometer'])\n", + "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", + "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", + "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", + "botometer_geral = df_botometer['analise_botometer']\n", + "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", + "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DzmZgqDkvEn9" + }, + "outputs": [], + "source": [ + "plt.figure(figsize =(20, 10)) #(11, 6)\n", + "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", + "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", + "c = 0\n", + "for i, bplot in enumerate(bplots['boxes']):\n", + " bplot.set(color=colors[c], linewidth=3)\n", + " c += 1\n", + " \n", + "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", + "c3 = 0\n", + "for cap in bplots['caps']:\n", + " cap.set(color=colorss[c3], linewidth=3)\n", + " c3 +=1\n", + "\n", + "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", + "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", + "plt.ylabel(\"Avaliação do Botometer\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mc5WWVrevEn9" + }, + "outputs": [], + "source": [ + "import scipy\n", + "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H-9ZYAcPvEn9" + }, + "outputs": [], + "source": [ + "scipy.stats.kruskal(res_geral, res_nao,res_sim)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "

Análise de Sentimento

" + ], + "metadata": { + "id": "_XSWWa_1lwQm" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install nltk\n", + "!pip install wordcloud" + ], + "metadata": { + "id": "Djeb7PUI77DC", + "outputId": "249f99d9-0620-4961-f224-ec93cf564d8b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 261, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n", + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (1.8.2.2)\n", + "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud) (1.21.6)\n", + "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud) (7.1.2)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from wordcloud) (3.2.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (1.4.4)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (2.8.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (3.0.9)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (4.1.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import string\n", + "import nltk\n", + "nltk.download('stopwords')\n", + "from nltk.corpus import stopwords\n", + "from nltk.probability import FreqDist\n", + "from wordcloud import WordCloud, STOPWORDS\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.feature_extraction.text import TfidfTransformer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score" + ], + "metadata": { + "id": "joxis7kss1II", + "outputId": "48106ef3-1340-4b79-aa71-80033a60b55f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 262, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_handles['Comportamento agressivo?'].head()" + ], + "metadata": { + "id": "LHgK1OAttT08", + "outputId": "655728aa-fc89-434f-9550-e4d2afc01d3e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 263, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 não\n", + "1 não\n", + "2 não\n", + "3 sim\n", + "4 não\n", + "Name: Comportamento agressivo?, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 263 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Seleção do texto e com o rótulo é agressivo ou não\n", + "df_result_merge_text = pd.merge(df_handles, df_users, on=['handle'])\n", + "df_result_merge_text = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", + "print(len(df_result_merge_text))\n", + "df_result_merge_text.head(1)" + ], + "metadata": { + "id": "UixBJa39kLDg", + "outputId": "c35ab42b-7256-4203-ded0-942c8d293909", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + } + }, + "execution_count": 264, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "834\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", + "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", + "\n", + " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não se aplica NaN \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "\n", + " Parece só Retweetar? ... lang location name \\\n", + "0 não ... 0.0 Brasil, São Paulo Leandro Mathes \n", + "\n", + " profile_image twitter_id \\\n", + "0 http://pbs.twimg.com/profile_images/1141547105... 52253248.0 \n", + "\n", + " twitter_is_protected verified withheld_in_countries tweet_author \\\n", + "0 0.0 0.0 [] lemathes \n", + "\n", + " tweet_text \n", + "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... \n", + "\n", + "[1 rows x 36 columns]" + ], + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...langlocationnameprofile_imagetwitter_idtwitter_is_protectedverifiedwithheld_in_countriestweet_authortweet_text
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...0.0Brasil, São PauloLeandro Matheshttp://pbs.twimg.com/profile_images/1141547105...52253248.00.00.0[]lemathes@LucianoHangBr Já demorou muito!, RT @LucianoH...
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 264 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_result_merge_text['Comportamento agressivo?'] = df_result_merge_text['Comportamento agressivo?'].str.lower()\n", + "df_result_merge_text['tweet_text'] = df_result_merge_text['tweet_text'].str.lower()" + ], + "metadata": { + "id": "Fy96MPEvyXCc" + }, + "execution_count": 265, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "3DxHVYbVpF6l" + } + }, + { + "cell_type": "code", "source": [ - "x_novo_trend['qtdtrends'] = df_result_merge['qtdtrends']\n", - "x_novo_trend['trends_media'] = df_result_merge['trends_media']\n", - "x_novo_trend['trends_max'] = df_result_merge['trends_max']" + "df_result_merge_text_analise=df_result_merge_text[['Comportamento agressivo?', 'tweet_author', 'tweet_text']]\n", + "print(\"\\nDimensões:\\n\")\n", + "print(\"Shape:\", df.shape)\n", + "print(\"\\nQuantidade de dados faltantes:\\n\")\n", + "print(df_result_merge_text_analise.isnull().sum())" + ], + "metadata": { + "id": "l0iDtJtBns0Q", + "outputId": "a26b88f9-ee7a-4ee1-f0c0-ca72b991ea02", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 266, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Dimensões:\n", + "\n", + "Shape: (834, 46)\n", + "\n", + "Quantidade de dados faltantes:\n", + "\n", + "Comportamento agressivo? 0\n", + "tweet_author 0\n", + "tweet_text 0\n", + "dtype: int64\n" + ] + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "df_result_merge_text_analise['Tamanho'] = df['tweet_text'].apply(len)\n", + "print(\"Tamanho dos comentários:\\n\")\n", + "df_result_merge_text_analise.head()" + ], "metadata": { - "id": "M0cQ547ivEn6" + "id": "es3Nmym3vG7o", + "outputId": "2a8d247a-5be3-4814-a7b9-7ba408de51c8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345 + } }, - "outputs": [], - "source": [ - "x_novo_trend.head()" + "execution_count": 267, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tamanho dos comentários:\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + "\n", + " tweet_text Tamanho \n", + "0 @lucianohangbr já demorou muito!, rt @lucianoh... 10004 \n", + "1 hospício....louca. https://t.co/34bby21hrq, . ... 7015 \n", + "2 rt @jairbolsonaro: - rio de janeiro / rj: o @g... 11420 \n", + "3 @rachelsherazade vc chama isso de jornalismo? ... 2846 \n", + "4 rt @brazilfight: janaína paschoal\\n\"jamais um ... 11465 " + ], + "text/html": [ + "\n", + "
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0nãolemathes@lucianohangbr já demorou muito!, rt @lucianoh...10004
1nãoMaurcio98905595hospício....louca. https://t.co/34bby21hrq, . ...7015
2nãoLunVianart @jairbolsonaro: - rio de janeiro / rj: o @g...11420
3simfelipeleixas@rachelsherazade vc chama isso de jornalismo? ...2846
4nãoJoseCar41451194rt @brazilfight: janaína paschoal\\n\"jamais um ...11465
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 267 + } ] }, { - "cell_type": "markdown", + "cell_type": "code", + "source": [ + "df_dist_grafico = plt.figure(figsize=(10,6))\n", + "sns.distplot(df_result_merge_text_analise['Tamanho'], kde=True, bins=50, color=\"green\")\n", + "plt.title('Distribuição do tamanho do texto')" + ], "metadata": { - "id": "jrJgWnrJvEn6" + "id": "ojUpwQTOvUFn", + "outputId": "77734e1d-b0ca-4e64-b61f-3b55a694435a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + } }, + "execution_count": 268, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição do tamanho do texto')" + ] + }, + "metadata": {}, + "execution_count": 268 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", "source": [ - "**Conjuntos de treinamento e teste**\n", - "\n", - "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." + "#total da likes\n", + "total = df_result_merge_text_analise['Comportamento agressivo?'].count()\n", + "total_sim = (df_result_merge_text_analise['Comportamento agressivo?']=='sim').sum()\n", + "total_nao = (df_result_merge_text_analise['Comportamento agressivo?']=='não').sum()\n", + "print(\"Total:\", total)\n", + "print(\"Total de sim:\", total_sim)\n", + "print(\"Total de não:\", total_nao)" + ], + "metadata": { + "id": "qu-5y7LFvou_", + "outputId": "8c454afc-8281-44c1-8f82-63d41a86c026", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 269, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total: 834\n", + "Total de sim: 98\n", + "Total de não: 735\n" + ] + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "texto = df_result_merge_text_analise[['Comportamento agressivo?', 'tweet_author', 'tweet_text']]\n", + "texto['Tamanho'] = texto['tweet_text'].apply(len)" + ], "metadata": { - "id": "r2Ydk5gJvEn6" + "id": "aOkaDiM60gxw" }, - "outputs": [], - "source": [ - "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " - ] + "execution_count": 270, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "stopWord = stopwords.words(\"portuguese\")" + ], "metadata": { - "id": "xmo-1VWTvEn6" + "id": "KG8nJnA80xhL" }, - "outputs": [], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] + "execution_count": 271, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "def remove_puntuacao_stopwords(texto):\n", + "\n", + " remove_puntacao = [word for word in texto.lower() if word not in string.punctuation]\n", + " remove_puntacao = ''.join(remove_puntacao)\n", + " return [word for word in remove_puntacao.split() if word not in stopWord]" + ], "metadata": { - "id": "ySGFI_0UvEn6" + "id": "XMAsFIAc09j3" }, - "outputs": [], - "source": [ - "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] + "execution_count": 272, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bg7cFJ8VvEn6" - }, - "outputs": [], "source": [ - "importances = classifier.feature_importances_\n", - "\n", - "indices = np.argsort(importances)\n", - "\n", - "fig, ax = plt.subplots(figsize =(10, 6))\n", - "ax.barh(range(len(importances)), importances[indices])\n", - "ax.set_yticks(range(len(importances)))\n", - "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" - ] - }, - { - "cell_type": "markdown", + "texto_preprocessado = texto.copy()\n", + "texto_preprocessado['tweet_text'] = texto['tweet_text'].apply(remove_puntuacao_stopwords)\n", + "texto_preprocessado['Tamanho'] = texto_preprocessado['tweet_text'].apply(len)\n", + "print(\"Tamanho dos comentários após aplicação do stopword:\\n\")\n", + "texto_preprocessado.head()" + ], "metadata": { - "id": "kN336CjPvEn6" + "id": "k0JVUzGr1Iv4", + "outputId": "3855d5dc-65ca-44ca-f2fd-f683cec0f5b6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + } }, - "source": [ - "**Resultados**\n", - "\n", - "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", - "\n", - "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", - "\n", - "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", - "\n", - "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." + "execution_count": 273, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tamanho dos comentários após aplicação do stopword:\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + "\n", + " tweet_text Tamanho \n", + "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", + "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", + "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", + "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 " + ], + "text/html": [ + "\n", + "
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Comportamento agressivo?tweet_authortweet_textTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 273 + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "df_dist_grafico_processado = plt.figure(figsize=(8,4))\n", + "sns.distplot(texto_preprocessado['Tamanho'], kde=True, bins=50, color=\"blue\")\n", + "plt.title('Distribuição do tamanho do texto após aplicação de STOPWORD')" + ], "metadata": { - "id": "MFWM1W5pvEn6" + "id": "3TSR5jYI1XCy", + "outputId": "f44a591b-4a74-4dd0-c3b9-1100b9957b97", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + } }, - "outputs": [], - "source": [ - "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" + "execution_count": 274, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição do tamanho do texto após aplicação de STOPWORD')" + ] + }, + "metadata": {}, + "execution_count": 274 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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D8ADx3gHoAbxsZrNTmWe6+ycFXot7iKr2yURj0tEFlnmASAS/JRqsHltgmfx9e4X47F9PJPwVVydUus956y2qxnrDibYVg4kagI2BvkVie5e4SuWBtOx0lv4++Gsq+9n0WR9NtDMpZCaRMH5OnHP/E9GI+ke1ogXimEZc0bQi8dpPI95rx7l7dT6zzxMNXnO3+UKaNipv2dOJ9/Z9xHfvM0T7hsOq2Ma1RJJb8Udqp/TempnWXw3oUejPX9YqWqqLLBczO464SiDTDl+kcUq1C/e5+7pVLVvGGAYQjVbPz2DbFwD/dfcfVWWLlEo1BLLcUlX958DuWcci0tjo8yc1pdH3bS414i7iMrjlOf8uIsvmOaJzpfwe+0SqRacMRERERKcMREREpJGfMmjXrp137Ngx6zBERERqxWuvvfaNuxfsW6ZRJwQdO3ZkzJgxWYchIiJSK8zss2LzdMpARERElBCIiIiIEgIRERFBCYGIiIighEBERERQQiAiIiIoIRARERGUEIiIiAhKCERERIRG3lOhiEg53HZb4emnnFK7cYhUh2oIRERERAmBiIiIKCEQERERlBCIiIgISghEREQEJQQiIiKCEgIRERFBCYGIiIighEBERERQQiAiIiIoIRARERGUEIiIiAhKCERERASNdigikrlioyOCRkiU2lPWGgIz621mH5jZeDPrX2D+Cmb2UJr/spl1zJl3Tpr+gZntk6atZ2b/NrP3zOxdMzszZ/k2ZjbMzD5K963LuW8iIiINSdkSAjNrCtwE7At0A44ys255i/UDprt7J+B64Jq0bjegL7AZ0Bu4OZW3EDjL3bsBOwKn5ZTZHxjh7p2BEem5iIiIlKCcNQTbA+Pd/RN3nw8MBPrkLdMHuDs9fgTY08wsTR/o7vPcfQIwHtje3ae4++sA7j4LGAd0KFDW3cDBZdovERGRBqecCUEHYGLO80ks+fH+0TLuvhD4Dmhbyrrp9MI2wMtp0lruPiU9/hJYq1BQZnaKmY0xszFTp06t3h6JiIg0UPXyKgMzWxUYDPzW3Wfmz3d3B7zQuu5+m7t3d/fua6yxRpkjFRERqR/KmRBMBtbLeb5umlZwGTNrBrQCplW2rpk1J5KB+9390ZxlvjKzddIy6wBf19ieiIiINHDlTAheBTqb2YZm1oJoJDgkb5khwAnp8eHAc+nf/RCgb7oKYUOgM/BKal9wBzDO3a+rpKwTgH/V+B6JiIg0UGXrh8DdF5rZ6cBQoClwp7u/a2aXAmPcfQjx436vmY0HviWSBtJyg4D3iCsLTnP3RWa2C3Ac8I6ZvZk2da67Pw1cDQwys37AZ8AR5do3ERGRhqasHROlH+qn86ZdmPN4LvCzIuteAVyRN+1FwIosPw3YczlDFhERaZTqZaNCERERqVnqulhEpJZU1kWxSNZUQyAiIiJKCEREREQJgYiIiKCEQERERFBCICIiIighEBEREZQQiIiICEoIREREBCUEIiIighICERERQQmBiIiIoIRAREREUEIgIiIiKCEQERERlBCIiIgISghEREQEJQQiIiICNMs6ABERKe622wpPP+WU2o1DGj7VEIiIiIgSAhEREVFCICIiIighEBEREZQQiIiICLrKQESkrNxh+nSYOxeaNoU11wSzrKMS+TElBCIiZTB/PvznP/D88zBlypLpa68NO+0Ee+4JzZtnF59IPiUEIiI17Isvov+AKVOgY0c48kho2RLmzIExY+Cxx+CNN6IvgbZts45WJCghEBGpQY8/DlddBSusAGecAZtttvT8nj3hzTfhrrvgiivgrLOgQ4dMQhVZihoViojUkKefhiOOiB/4Cy74cTJQYeut4bzzoFkzuOkmmDmzduMUKUQJgYhIDRg1Cg47DLbYImoGWrWqfPk114RTT41k4NZbYcGC2olTpBglBCIiy2nyZDj8cNhgAxg6FFZeubT1OnaEE06Ajz+GZ58ta4giVVJCICKyHBYsiEaD338fjQXbtave+j16wHbbxemGr78uT4wipVCjQhGRKlQ24uAFF8TlhQMHQteuy1b+EUfAu+/Cgw/G6Qb1UyBZUA2BiMgyGj0arr0WTj45agmW1eqrQ58+8N578PbbNRefSHUoIRARWQYLFsBJJ8UVBX/+8/KXt9tucbrhySejd0OR2qZTBiIiy+DJJ+H99+HMM+N0wfJq2hT23RfuvRfGjo2rFURqk2oIRESq6csvYdiw6IK4W7eaK3fHHaPnwqeeUi2B1L6yJgRm1tvMPjCz8WbWv8D8FczsoTT/ZTPrmDPvnDT9AzPbJ2f6nWb2tZmNzSvrYjObbGZvptt+5dw3EWmc3KNGoEULOPTQmi27WTPo3RsmTIjaB5HaVLaEwMyaAjcB+wLdgKPMLD+X7gdMd/dOwPXANWndbkBfYDOgN3BzKg9gQJpWyPXuvnW6PV2T+yMiAjEGwbhxcNBBsNpqNV/+TjvBqqvCyJE1X7ZIZcpZQ7A9MN7dP3H3+cBAoE/eMn2Au9PjR4A9zczS9IHuPs/dJwDjU3m4+yjg2zLGLSJS0MKFMHgwrLtuNAIsh+bNYZdd4K234Ft900ktKmdC0AGYmPN8UppWcBl3Xwh8B7Qtcd1CTjezt9NphdaFFjCzU8xsjJmNmTp1aml7IiJCdE/8zTdxqqBp06qXX1a77hr3L7xQvm2I5GtIjQpvATYGtgamAH8ptJC73+bu3d29+xprrFGb8YlIPTZ3bvQmuOmmNduQsJB27WDzzeHFF6NWQqQ2lDMhmAysl/N83TSt4DJm1gxoBUwrcd2luPtX7r7I3RcDt5NOMYiI1IRhw2DWrKgdqI2eBHv2jIGP3nyz/NsSgfImBK8Cnc1sQzNrQTQSHJK3zBDghPT4cOA5d/c0vW+6CmFDoDPwSmUbM7N1cp4eAowttqyISHXMnBkJwXbbxYBEtaFbt+jBcPTo2tmeSNkSgtQm4HRgKDAOGOTu75rZpWZ2UFrsDqCtmY0Hfg/0T+u+CwwC3gOeAU5z90UAZvYg8BKwqZlNMrN+qaw/mdk7ZvY2sDvwu3Ltm4g0Lk89FT0T9slvFl1GTZrA9tvHGAezZtXedqXxKmtPhenSv6fzpl2Y83gu8LMi614BXFFg+lFFlj9uuYIVESng44+jMeEuu8Baa9XutnfYIYZFHjMGdt+9drctjU9DalQoIlLjzj8/Ogw64IDa3/a668bt5Zdrf9vS+CghEBEp4rXXolfCPfeEVq2yiWGHHaLnwq++ymb70ngoIRARKeKcc2JsgX32qXrZcunRI65qGDMmuxikcVBCICJSwLBhcTvvPFhppeziaN0aNtoIXn89uxikcVBCICKSZ/Fi6N8fNtgATj0162hg221h0iRQ56pSTkoIRETyPPxw/CO/7DJYYYWso4Fttol71RJIOSkhEBHJMX9+nCbYYgs4+uisowlt20ZthRICKSclBCIiOW6/PfoeuPrq8g5gVF3bbguffqoREKV8lBCIiCSzZ8Oll8bQxvvum3U0S6s4bfDGG9nGIQ2XEgIRkeS66+Drr6N2oDYGMKqOtdaCddaBt9/OOhJpqJQQiIgQicC118ZohjvumHU0hW2xBXz4IfzwQ9aRSENUUkJgZo+a2f5mpgRCRBqkK66A77+P+7pqyy3jksj33ss6EmmISv2Bvxk4GvjIzK42s03LGJOISK366CO45Rbo1w+6dMk6muI22ghWWUWnDaQ8SkoI3H24ux8DbAt8Cgw3s/+a2Ulm1rycAYqIlNvZZ0OLFtGgsC5r2hQ23xzGjoVFi7KORhqakoc/NrO2wLHAccAbwP3ALsAJQM9yBCciUm6jRsFjj8FBB8GQIVlHU7UttojRD0ePhp/8JOtopCEptQ3BY8ALwMrAge5+kLs/5O6/AVYtZ4AiIuWyeDGcdRZ06AB77ZV1NKXZfHNo0gSeeirrSKShKbUNwe3u3s3dr3L3KQBmtgKAu3cvW3QiImX04IMxiuCVV8Ypg/pgpZWiLcGzz2YdiTQ0pSYElxeY9lJNBiIiUpt++CGGN952Wzj22KyjqZ5u3aIbYw12JDWp0oTAzNY2s+2AlcxsGzPbNt16EqcPRETqpeuvh4kT4S9/iSr4+qRbN3CH4cOzjkQakqoaFe4DnAisC1yXM30WcG6ZYhIRKavJk+Gqq6IhYc+eWUdTfRtsAG3awNChcNRRWUcjDUWlCYG73w3cbWaHufvgWopJRKSszjoLFiyIrorroyZNoFevaEfgXve6WZb6qdKEwMyOdff7gI5m9vv8+e5eTz9OItJYDRsGDz0El1wCG2+cdTTLbp99YNCg6JNgiy2yjkYagqrOnK2S7lcFWha4iYjUG/PmwWmnQadO8Mc/Zh3N8tl777gfOjTbOKThqOqUwT/S/SW1E46ISPn8+c/RTfEzz8CKK2YdzfJZd91oXPjss/CHP2QdjTQEpXZM9CczW83MmpvZCDObamb17EIdEWnMJkyAyy+Hww+P6vaGYJ99oqfF77/POhJpCErtunhvd/+jmR1CjGVwKDAKuK9cgYmIlOK22wpPP+WUJY/d4YwzYiyA66+vnbjK7bbbYOHCOA3Sv3/0YAhL77dIdZR69W1F4rA/8LC7f1emeEREatyDD8KTT0ZDwnXXzTqamrPJJtCsmYZDlppRag3Bk2b2PvAD8GszWwOYW76wRERqxpQpcPrpsOOO8NvfZh1NzWrRAjp3VkIgNaPU4Y/7AzsD3d19ATAH6FPOwERElpd7VKH/8AMMGBCnDBqabt0i6fn226wjkfquOh12dgGONLPjgcOBvcsTkohIzbj77jhVcNVVsOmmWUdTHpttFveqJZDlVdIpAzO7F9gYeBNYlCY7cE+Z4hIRWS6TJsGZZ8Kuu0aDwoaqfXtYbTV4/33YZZeso5H6rNQ2BN2Bbu7u5QxGRKQmuMMvfhGt8O+6q/4NXlQdZtC1a9QQLF6cdTRSn5WaEIwF1gamlDEWEZEaMWJE9ODXt288HjFiybyGeFle167w8ssxaJPIsio1IWgHvGdmrwDzKia6+0FliUpEZBlNmACDB8PWW9fPkQyXRZcucf/++9nGIfVbqQnBxeUMQkSkJsyZA7ffDq1bw/HHN55RAFu3hrXXhnHjso5E6rNSLzt8nuihsHl6/CrwehnjEhGpFve4qmDGDDj5ZFhllarXaUi6dIlxGubNq3pZkUJKvcrgZOAUoA1xtUEH4FZgz/KFJiL1XSndCteUESPgrbfgZz+DDTes+fLrum7dYORIGD0adtst62ikPiq17e1pwE+AmQDu/hGwZrmCEhGpjvHj4dFHYautYM9G+jdlk03iaorhw7OOROqrUhOCee4+v+KJmTUj+iGolJn1NrMPzGy8mfUvMH8FM3sozX/ZzDrmzDsnTf/AzPbJmX6nmX1tZmPzympjZsPM7KN037rEfROReuzbb+HWW6FtWzjhhMbTbiDfSitBx44wbFjWkUh9VWqjwufN7FxgJTPbCzgVeKKyFcysKXATsBcwCXjVzIa4e25/Wv2A6e7eycz6AtcQvSF2A/oCmwHtgeFmtom7LwIGADfy406R+gMj3P3qlHz0B84ucf9EpI4rdPph3jy46abob+C00xpfu4F8XbvC//1ftKNYffWso5H6ptQagv7AVOAd4JfA08D5VayzPTDe3T9JtQsD+fH4B32Au9PjR4A9zczS9IHuPs/dJwDjU3m4+yigUK/duWXdDRxc4r6JSD20eHGMTzB5cnRCtPbaWUeUvS5d4nUZOTLrSKQ+KvUqg8XA48Cp7n64u99eQq+FHYCJOc8npWkFl3H3hcB3QNsS1823lrtXdJz0JbBWoYXM7BQzG2NmY6ZOnVpFkSJSVz31FLz+Ohx2GGy+edbR1A0bbQQrr6x2BLJsKk0ILFxsZt8AHwAfmNlUM7uwdsJbNilZKZiwuPtt7t7d3buvscYatRyZiNSEV16JQYt22gl69co6mrqjWbO4wkAJgSyLqmoIfkdcXdDD3du4extgB+AnZva7KtadDKyX83zdNK3gMqmhYitgWonr5vvKzNZJZa0DfF3F8iJSD733Xpwq2GQTOOaYxtuIsJheveCDD2DixKqXFclVVUJwHHBUOo8PgLt/AhwLHF/Fum9XX6wAABm9SURBVK8Cnc1sQzNrQTQSHJK3zBDghPT4cOC59O9+CNA3XYWwIdAZeKWK7eWWdQLwryqWF5F65vPP44qCddaBU0+F5s2zjqjuqagxyR2/QaQUVSUEzd39m/yJ7j4VqPSjmNoEnA4MBcYBg9z9XTO71MwqxkC4A2hrZuOB3xONF3H3d4FBwHvAM8Bp6QoDzOxB4CVgUzObZGb9UllXA3uZ2UdAr/RcRBqIqVPhb3+DVVeF3/wmLrOTH9t8c1hzTZ02kOqr6rLD+cs4DwB3f5q4IiF32oU5j+cCPyuy7hXAFQWmH1Vk+Wmo50SRBumrryIZWLwYzjhDl9RVpkmT6Jxp+PDozlmnVKRUVdUQbGVmMwvcZgFb1EaAItK4ffNNVIPPmAGnn67LC0vRq1ckUe++m3UkUp9UWkPg7k1rKxARkXzffgt77RVdE592WlxWJ1WraEcwfLguyZTSldoxkYhIrZoxA/beO64q+Ne/otMdKc3660PnzmpHINVTatfFIiK1ZuZM2HdfePtteOyxSAyKjZxYXTVVTl3Xqxfccw8sWKCrMaQ0qiEQkTpl2rT4MXv1VXjoIdh//6wjqp969YI5c+Dll7OOROoLJQQiUmdMmQI9ey6pGTjkkKwjqr92313DIUv1KCEQkTrh009h111hwgR4+mk48MCsI6rfWreG7t2VEEjplBCISObGjYNddomrCkaMgD32yDqihqFXLxg9OtpkiFRFCYGIZOq552KQooULY9jeHXbIOqKGo1cvWLQIRo3KOhKpD5QQiEhm7roL9tkHOnSIf7Jbbpl1RA3LTjtFF886bSClUEIgIrVu8WI4/3z4+c+jEeF//gMdO2YdVcOz4orRLmPYsKwjkfpACYGI1Kq5c+GII+CKK6Bfv2hAqLEJyqdXr+jc6Ysvso5E6jolBCJSa774Aq68Eh5/HP78Z7j9dnWaU24aDllKpYRARGrFq6/C1VfD99/Hj9NZZ2kkvtqw1VbQtq3aEUjV1HWxSANRrEveU06p3TjyzZ8PgwfHFQQbbxzx7LZbtjE1JhoOWUqlhEBEyubzz+HOO6MHwj33hEMPhWb61ql1vXrBoEHw/vvQtWvW0UhdpY+miNS4RYvg2mvjFEHLlnDmmdCtW9ZRNV65wyErIZBilBCIyFIqGw2wlNMP774by/33v7DttnDssbDKKjUXn1TfhhvCRhtFQvCb32QdjdRVSghEpEbMnRtXEFTUCtxzTzQg1DnruqFXL3jwwegRUqdtpBBdZSAiy+3552HrreGyy6KPgXHj4LjjlAzUJb16waxZcbWHSCFKCERkmX32GRx5ZPQ2OG8ePPMM3HcfrLlm1pFJvt13jwRNlx9KMao4EpFq+/57+NOf4Jpr4vlFF8Ef/wgrr7x85VbWfkGWT7t2sM02MHQoXHBB1tFIXaQaAhEp2cKFcRnhppvCJZfAQQfFpWwXX7z8yYCU3/77w0svwbRpWUcidZESAhGpkju8+WaMRtivH7RvHx0NPfQQbLBB1tFJqfbfPwaWGjo060ikLtIpAxGp1IcfwmOPwSefRM3AI49EB0NqMFj/9OgBa6wBTz4JRx+ddTRS1yghEJGCPv0UnngCxo6N0QiPOy5OFxS6ZE3n/uuHJk2iluDxx3X5ofyY3g4ispRPP41/kO+8Ex0KHXpotFBv0UI/IA3BAQfAgAHRcdRPf5p1NFKX6OMtIgCMGRMNBZ98MhKBgw+ORGDFFbOOTGrSXnvFkNNPPaWEQJamhECkBtXVEQcrk5sItGkDffpEIrDSSuXbpk4xZGe11SIRePLJJZeNioCuMhBptF5+GQ48MBqa/ec/cMUVMGEC7LdfeZMByd4BB8B770VDUZEKSghEGhF3eO656MZ2xx0jEbj88mg3cO658e9RGr4DDoj7p57KNg6pW5QQiDQC7lFFvPPOsOeeMSLhtddG18PnnadEoLHp1Ak22UQJgSxNbQhEGrDFi6PzoCuvhLffjk6Ebr4ZTjopRiN88MGsI5SaVmo7lgMOgBtvhNmzYdVVyx+X1H2qIRBpgObPh1Gjokvhvn1j4KEBA+Cjj+DXv9aVAxIJwfz5GuxIllANgUgD8t130aXw88/DnDmw/vrw8MNwyCHQtGnW0UldsssucaroySfjElMRJQQiDcDbb0cNwKuvwqJFMeZAr17QuTMcfnjW0Uld1Lw59O4dvVEuWqSEUZQQiNRb8+bBo4/CP/4RNQLNm8NPfhKNBtdaK+vopD449FAYNCiuNlEnRaKEQKSeGTcO7rgjagSmTYONNoKrr46uhVdZJevopD7Zbz9YYQUYPFgJgZS5UaGZ9TazD8xsvJn1LzB/BTN7KM1/2cw65sw7J03/wMz2qapMMxtgZhPM7M1027qc+yZSm778Eq6/HrbbDrp1g7/+FXr2hGefjYaCZ5+tZECqr2XLOG3w6KNxRYo0bmWrITCzpsBNwF7AJOBVMxvi7u/lLNYPmO7uncysL3ANcKSZdQP6ApsB7YHhZrZJWqeyMv+fuz9Srn2SxqWy7nXL3RWxe/QV8PTTcXvhhfjC7t4dbrghrhwo9bSAugkWKP4+OOww+Ne/4JVXorMqabzKecpge2C8u38CYGYDgT5AbkLQB7g4PX4EuNHMLE0f6O7zgAlmNj6VRwllitRLc+ZEL4JPPRVJwMSJMX2rraIXwWOOgS5dso1RGp4DD4z2J4MHKyFo7MqZEHQAJuY8nwTsUGwZd19oZt8BbdP00XnrdkiPKyvzCjO7EBgB9E8JhUid9dFHS2oBRo6M68JXXTVGpLvwQth3X+jQocpiRJbZ6qtHQ9RHHoE//QnMso5IstKQGhWeA3wJtABuA84GLs1fyMxOAU4BWH/99WszPmnEKqprFyyADz+EsWPj9vXXMX2ddWC33WDzzaNb2WbN4hRBoa5lsxw5UacfGqa+feHEE2H0aNhpp6yjkayUMyGYDKyX83zdNK3QMpPMrBnQCphWxboFp7v7lDRtnpndBfyhUFDufhuRMNC9e3ev3i6JVN+0afHj/8478P77kRQ0bx7V/3vuGUlAu3ZZRymN2SGHwK9+BQ88oISgMStnQvAq0NnMNiR+tPsCR+ctMwQ4AXgJOBx4zt3dzIYAD5jZdUSjws7AK4AVK9PM1nH3KakNwsHA2DLum0hRixfHsLJvvRVJwJSUqrZrF/0EbLFFDCzTokW2cYpUWG21aEvw0ENxNUuzhlR3LCUr22FPbQJOB4YCTYE73f1dM7sUGOPuQ4A7gHtTo8FviR940nKDiMaCC4HT3H0RQKEy0ybvN7M1iKThTeBX5do3kXzffw/DhsHdd0cSMGtW9PzWuXN0Ebv55nFVgM7PSl119NHRzfWIEbDPPlUvLw1PWfNAd38aeDpv2oU5j+cCPyuy7hXAFaWUmabvsbzxilTHrFnR7eugQdEfwA8/wEorxY//VlvF/UorZR2lSGn23RdatYrTBkoIGidVDIlUw/z5UQNw+OHR4G/u3LgK4Be/gD59osGg+oSX+miFFeJ9PXAg3HSThkRujJQQiFRhwYLoJGjMmBhEaN68qP7/xS/gyCNh552hSerz8+OPs41VZHmcdFJ0i/3ww/FYGhclBCIFuMP48fDSS/Daa3E6YJVVYPvto7fAa69VTYA0PDvvDJtuCnfeqYSgMVJCIA1OsWvlS7l+f/x4uPfeuE2YENWo22wDPXpA165LkgAlA9IQmcHPfx5jY3z4YVwNI42HEgJp9KZPj4aB99wD//1vfCn26hWDB22zTSQFIo3FccdFV9l33QVXXZV1NFKbyjraoUhd5Q7//jccdRSsvXZ0yjJjBlxzDXz+eVw1sOOOSgak8VlnnRgWecCAaEQrjYdqCKReWpYudG+7DWbOjHYBL74Y3QavvDL88pfRbes226ifABGIBPmJJ2JY5L59s45GaosSAmnw3ON86MiR8Oab0ZNg586w//6w7bZw+ulZRyhSt/TuHWNq/P3vSggaEyUE0mAtXBiXCg4fHkMJr7IK7LEH7LprnCYQkcKaNIHTToPf/Q5efz0SZ2n4lBBIgzNnDrzwQrQRmDEjzoked1xcMqjxA0RKc+KJcN55UUtw111ZRyO1QQmBNBgzZkRjwBdeiMZQXbtGItCt25KOgxoCDUEstWH11eH44yMZuPLKSKylYVNCIPXe9OkwdGgkAosXww47wF57RZfCIrLszjorEtAbbogrcKRhU0Ig9dbs2TGewKhRkQjstFMM0LLGGllHJtIwdOoERxwBN98M/ftD69ZZRyTlpIRA6oTq9C44b14MNfz009Gl8M47x3XT7dqVN0aRxqh//yUDHp1/ftbRSDk1oDOr0tC5wyOPRJuARx6BDTeECy6I85xKBkTKY6ut4hLdG26IIb+l4VJCIPXCK6/E5YI/+1l0JnTGGXFTOwGR8rvoIpg2Df7yl6wjkXJSQiB12mefwTHHREPBjz6KUwtvvAGbbZZ1ZCKNR48ecPjhkRB8/XXW0Ui5mLtnHUNmunfv7mPGjMk6DOHHbQh++AGeeSb6EjCL1s5nnw0tWxZeXkRqXm4bng8/jNN1p54Kf/tbdjHJ8jGz19y9e6F5alQodcqiRTHOwBNPxPnKY4+Na6DXWy/ryEQat002gX794NZbIyno0iXriKSm6ZSB1AnuMHYsXHYZPPAArLUWnHMO3HuvkgGRuuKyy6IL8FNPjc+sNCyqIZBlVlm1faHLBYt55x34619h3DhYc80YaW3rrTXyoEhds+aacNVV8OtfR+J+zDFZRyQ1STUEkpkvvoCTT44f/88+iysILrpIwxCL1GUnnxzjgvz+93HlgTQcqiGQWjdzJlx7LVx3HSxYEJcPrr9+VEWKSN3WtGnUDvboETWBjzyiBL6hUA2B1JoFC6K3s06d4PLL4cAD4zTB9dcrGRCpT7baCq64Ah59VCMhNiSqIZCyc4fBg+Hcc6Mvgd12ixqCHj2qXleXF4rUTWedBf/3f1HDt8MO6hukIVANgZSNOwwZAtttF+0DWrSAJ5+MvgVKSQZEpO5q0iSuAmrZEg46SO0JGgIlBFLj3GPgoe23hz59os3A3XfDm29Gn+g63yjSMHToAI8/DpMnR0+G8+ZlHZEsDyUEUmPc4b33Ytz0/feHb76BO++E99+PAYia6QSVSIOzww7wz3/CyJFw5JHRVkjqJ31Fy3JbvDjGF3jmGfj8c2jTBm6/HU44AZo3zzo6ESm3Y4+FGTPgN7+Jvgnuv1+f/fpICYEsswUL4KWXYNiwGPBkzTXji2GnneAXv8g6OhGpTaefDvPnR2PDGTPg4YehVauso5LqUEIg1TZ9OvzjH9Fj2cyZ0LEj/PKX0cFQE52EEmm0fv97aN06+if4yU/i6qJNN806KimVEgL5n2KX+FV0Q/z223DjjXDffTEaYbdu0Lt3DHqS31BQlwuKNFyVfVecdBJssEFcWbTttnDDDVFjWKgxcU11fy41Q//npFKLFkVPZLvtFp2R3HdfnCN86y0488zI/nXVgIjk2mOPGKNkp53ih3333eMPhdRtSgikoKlTYwji886LTP/zz6MzoUmTosHglltmHaGI1GXt28Ozz8ZwyWPHxhglxxwTj6Vu0ikD+Z+5c+H116Oh4Icfxj//rl1hwIC4jLBp06wjFJH6pEmTaF90xBHR5uiWW2KUxJ49oV+/+M5ZccWso5QKSggauQUL4vrh+++HgQOjY5E114wOhXbcMS4hPOigrKMUkfqsdWv405+gf/9okPzPf8Jxx0XfJF27RnukTp2ioyP98ciOEoJGaO7cuFRw8ODoWnj69Oh+tEcP2Hln2GgjtQsQkZrXpg2ccw6cfTa8+CJcckm0LXjnnZi/4orx/bPeerDyyjE+QpcusNJK2cbdWCghaATc4d1343zes8/C889HUtCqVfz7P+ww2Hvv6JdcRKTcmjSBn/40ejY88kj49lv4+OMY/Ozjj2H4cBg6NJY1iwRhww2X3Dp2XPK4fXtd7lxTlBA0QHPmwGuvRVuAitvXX8e8Ll3inF7v3tESuEWLqsvTJYQijVNtffbbtIlbxaBnixbBV1/BlCnwxRfRyHnSpLi6acaMpddt1ixOSbRuDauvvuRxxfMzz4Q11lDSUIqyJgRm1hv4K9AU+Ke7X503fwXgHmA7YBpwpLt/muadA/QDFgFnuPvQyso0sw2BgUBb4DXgOHefX879y9LixfGBmTAhbuPGRevdsWPhk0+iVgCgc+f48d9116gFWH/9bOMWEalK06bxz799+xgtNdeCBTGy4jffLLmfPj1uH38cCcOiRUuWv+qq6Ea5QwdYd92lb+3bR7JQcWvTpnGPuVK2XTezpsBNwF7AJOBVMxvi7u/lLNYPmO7uncysL3ANcKSZdQP6ApsB7YHhZrZJWqdYmdcA17v7QDO7NZV9S7n2rxSLFy+5LVq09P3ixbBwYXTwU3H7/vulH3//fVSlTZ0at2++WfJ44sSlRxZr2jQ6CNp22xhIaJtt4hrgdu2y238RkZrWvDmsvXbcClm8GGbPXpIkbLZZfF9OmhS3V1+Fxx4rPDKjWdQsVCQI7dpF+6pit1VXjXYPLVpEXC1aFL81aRI3s7jlPq4rbbbKmQttD4x3908AzGwg0AfITQj6ABenx48AN5qZpekD3X0eMMHMxqfyKFSmmY0D9gCOTsvcncqttYTg0kvhyiuX/sGvKc2bL/0G3W47OPjgJefSOnaEjTeGFVaouW2KiNRHTZrAaqvFbYMNCvd46B61CxWnI3L/bOU+/vhjmDVryW1+Geucc5OE3GRh663jtG9tKGdC0AGYmPN8ErBDsWXcfaGZfUdU+XcARuet2yE9LlRmW2CGuy8ssPxSzOwUoOItMtvMPqjGPmViwYJ4437xRVk30w74pqxbkFLoOGRPx2AZ/fKXNVpcjRyHGo6pbNyXPtVRYfToGq9B2KDYjEZ3tsTdbwPUTC6PmY1x9+5Zx9HY6ThkT8egbtBxqH3lbHc5GVgv5/m6aVrBZcysGdCKaFxYbN1i06cBq6cyim1LREREiihnQvAq0NnMNjSzFkQjwSF5ywwBTkiPDweec3dP0/ua2Qrp6oHOwCvFykzr/DuVQSrzX2XcNxERkQalbKcMUpuA04GhxCWCd7r7u2Z2KTDG3YcAdwD3pkaD3xI/8KTlBhENEBcCp7n7IoBCZaZNng0MNLPLgTdS2VI6nUapG3QcsqdjUDfoONQy84oL1kVERKTRUt9NIiIiooRARERElBA0embW28w+MLPxZtY/63gaOjP71MzeMbM3zWxMmtbGzIaZ2UfpvnWabmb2t3Rs3jazbbONvv4yszvN7GszG5szrdqvu5mdkJb/yMxOKLQtKa7IcbjYzCanz8SbZrZfzrxz0nH4wMz2yZmu760yUELQiOV0L70v0A04KnUbLeW1u7tvnXONdX9ghLt3Bkak5xDHpXO6nULGXXHXcwOA3nnTqvW6m1kb4CKiM7TtgYsqkggp2QB+fBwgup3fOt2eBsjrwr43cLOZNdX3VvkoIWjc/te9dBoIqqJ7aaldfYjutkn3B+dMv8fDaKKvjXWyCLC+c/dRxJVMuar7uu8DDHP3b919OjCMwj9uUkSR41DM/7qwd/cJQEUX9vreKhMlBI1boe6lC3b5LDXGgWfN7LXUjTbAWu4+JT3+ElgrPdbxKa/qvu46HuVzejo9c2dOrYuOQy1TQiBSu3Zx922J6s7TzOynuTNTJ1u6FriW6XXP1C3AxsDWwBTgL9mG03gpIWjcSuleWmqQu09O918DjxHVn19VnApI91+nxXV8yqu6r7uORxm4+1fuvsjdFwO3s2RkWx2HWqaEoHErpXtpqSFmtoqZtax4DOwNjGXpLrxzu90eAhyfWr3vCHyXU8Uty6+6r/tQYG8za52qtfdO02Q55LWLOYT4TEA1u7CvzZgbqkY32qEsUax76YzDasjWAh6zGMu0GfCAuz9jZq8Cg8ysH/AZcERa/mlgP6Ix1ffASbUfcsNgZg8CPYF2ZjaJuFrgaqrxurv7t2Z2GfGDBHCpu5faQE4oehx6mtnWxCmbT4FfwjJ3YS/LQV0Xi4iIiE4ZiIiIiBICERERQQmBiIiIoIRAREREUEIgIiIi6LJDEamEmbUlBv4BWBtYBExNz7dPfcmXO4aLgdnu/udyb0ukMVNCICJFufs0oktZ/TCLNHA6ZSAi1WJmJ5vZq2b2lpkNNrOV0/QBZnaLmY02s0/MrGcarGacmQ3IWf8WMxtjZu+a2SU50z81s0vM7HUze8fMuuRstpuZjUzlnpGzzu/NbGy6/bY29l+koVJCICLV9ai793D3rYBxQL+cea2BnYDfEd3JXk+MZ79F6o0O4Dx37w5sCexmZlvmrP9NGvzpFuAPOdO7EMMPbw9cZGbNzWw7ohfBHYAdgZPNbJsa3leRRkMJgYhU1+Zm9oKZvQMcQ/zgV3gijRz4DvCVu7+TBq15F+iYljnCzF4H3kjrdstZ/9F0/1rO8gBPufs8d/+GGIRoLWAX4DF3n+Pus9O6u9bgfoo0KmpDICLVNQA42N3fMrMTib7pK8xL94tzHlc8b5YGqfkD0MPdp6dTCSsWWH8RS38/5ZaVP09EaoBqCESkuloCU8ysOVFDUB2rAXOA78xsLWDf5YjjBeBgM1s5jR55SJomIstAWbaIVNcFwMvE5YcvEwlCSVKtwhvA+8BE4D/LGoS7v55qGF5Jk/7p7m8sa3kijZ1GOxQRERGdMhARERElBCIiIoISAhEREUEJgYiIiKCEQERERFBCICIiIighEBEREeD/AzaODR0MO3/vAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MMZ0DPRDvEn7" - }, - "outputs": [], "source": [ - "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " - ] + "def grafico_frequencia(data):\n", + " plt.figure(figsize=(10,5))\n", + " FreqDist(np.concatenate(data.tweet_text.reset_index(drop=True))).plot(25, cumulative=False, color=\"green\")" + ], + "metadata": { + "id": "dW1fnIfj1lsj" + }, + "execution_count": 275, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = sim:\\n\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='sim'])" + ], "metadata": { - "id": "XPiyVsitvEn7" + "id": "1A0s6yxv1wye", + "outputId": "c7bd4450-b0ba-4a73-bbdf-ae664c16a7d0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + } }, - "outputs": [], - "source": [ - "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "#classifier = classifier.fit(x_train,y_train)\n", - "#y_pred = classifier.predict(x_test)\n", - "#mean = np.mean(y_pred == y_test)\n", - "#balanced = balanced_accuracy_score(y_test, y_pred)\n", - "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "#confusion_matrix(y_test, y_pred)" + "execution_count": 276, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gráfico de frequência de Comportamento agressivo = sim:\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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Z2Rwz2z13GW6m6O6bY6L2WmCqmeXGxu0NrIq3V8XEjXh+CrAhv7yfx5S9/P0+u727xLURERGRSjLcRO0M4NPAvYSuzKVA82APMLM9zGxqvD0JeDPwJCFhy80YPQO4Lt6+Ph4Tz98es8zrgffHWaH7AwcDS4ZZ75KbWDORqROn0tndyabtm0pdHREREakgg876zHH3/YuIPQe4Mo5TqwKucfcbzOwJ4NdmdjHwEKErk3j9CzNbDmwkzPTE3R83s2uAJ4BO4ByvkBmfObMaZrF5x2ZWb1vN9Prppa6OiIiIVIhhJWpmdnp/5e5+1UCPcfdHgSP6KV9BP7M23X0H8N4BYn0F+Mpw6lqOZjfO5ukNT7N622peNfNVpa6OiIiIVIhhJWrAkXm3JwInAA8CAyZq0ktrqYmIiEgxhtv1+S/5x3Hs2a8zqdEYNKuhd0KBiIiIyHANdzJBoVagmHFr45Ja1ERERKQYwx2jtoCwqwCEzdhfCVyTVaXGGiVqIiIiUozhjlH7dt7tTmClxx0EZGi5RE1dnyIiIjISw+r69LA5+1PAZGAa0JFlpcaa3KK3alETERGRkRhWomZmpxAWmX0vcApwv5mdPPijJEddnyIiIlKM4XZ9fh440t3XQth1ALgNuHbQRwkAe9TvgWGsa11HZ3cnNVXD/bWLiIjIeDbcWZ9VuSQt2jCCx457tdW1TK+fjuOsb1tf6uqIiIhIhRhusnWzmd1iZmea2ZnAH4Abs6vW2KPuTxERERmpQRM1MzvIzF7v7p8GfgocHi9/Ai4dhfqNGUrUREREZKSGGiz1PeBzAO7+O+B3AGb2N/HcOzKt3RjSs0THNi3RISIiIsMzVNfnLHd/rLAwlu2XSY3GqNw2UmpRExERkeEaKlGbOsi5SWlWZKxT16eIiIiM1FCJWrOZfbSw0Mw+AizNpkpjU0+i1qpETURERIZnqDFqnwB+b2YfoDcxmwfUAe/OsmJjTa7rU2PUREREZLgGTdTcfQ3wOjM7HjgsFv/B3W/PvGZjjLo+RUREZKSGtUS+u98B3JFxXcY0JWoiIiIyUtpdYJRMr59OtVWzaccm2jvbS10dERERqQBK1EZJlVUxs2EmAGtb1w5xbxERERElaqNK3Z8iIiIyEkrURpESNRERERkJJWqjaFZjXKKjVUt0iIiIyNCUqI2i2Q1qURMREZHhU6I2itT1KSIiIiOhRG0UqetTRERERkKJ2ihSi5qIiIiMhBK1UaRETUREREZCidooUqImIiIiI6FEbRRNmTCFuuo6tnVso7WjtdTVERERkTKnRG0UmVlPq5omFIiIiMhQlKiNMnV/ioiIyHApURtlsxriEh3b1KImIiIig1OiNsrUoiYiIiLDpURtlClRExERkeFSojbKNJlAREREhiuzRM3M9jGzO8zsCTN73MzOjeW7m9lCM3smXk+L5WZmPzCz5Wb2qJm9Ji/WGfH+z5jZGVnVeTTkxqipRU1ERESGkmWLWifwKXc/FDgGOMfMDgU+Cyxy94OBRfEY4K3AwfFyNnAJhMQOuBA4GjgKuDCX3FUidX2KiIjIcGWWqLn7S+7+YLzdAjwJ7AWcBFwZ73Yl8K54+yTgKg/uA6aa2RzgLcBCd9/o7puAhcCJWdU7a0rUREREZLjM3bN/ErP9gLuAw4Dn3X1qLDdgk7tPNbMbgK+7++J4bhHwGeA4YKK7XxzLLwC2u/u3C57jbEJLHHPmzGlasGBB5j9XW1sb9fX1I3tMZxtvuPkNTKiawOK3Lib8CpLFHPI5FTPVmFnFVUzFLPe4ijk+Y2YVdzzHLDRv3ryl7j6v35PunukFaASWAu+Jx5sLzm+K1zcAx+aVLwLmAecDX8grvwA4f7DnbGpq8tHQ3Nxc1OMavtLgXIRv3r45tZiDUcz0VUpdFXN8xswqrmKOz5hZxR3PMQsBzT5ATpPprE8zqwX+F7ja3X8Xi9fELk3i9dpYvgrYJ+/he8eygcorlro/RUREZDiynPVpwGXAk+7+n3mnrgdyMzfPAK7LKz89zv48Btji7i8BtwDzzWxanEQwP5ZVLC3RISIiIsNRk2Hs1wMfAh4zs4dj2b8DXweuMbOzgJXAKfHcjcDbgOVAG/BhAHffaGZfBh6I9/uSu2/MsN6Zm9WoJTpERERkaJklah4mBdgAp0/o5/4OnDNArMuBy9OrXWnNblDXp4iIiAxNOxOUgMaoiYiIyHAoUSuBXNfnmm0aoyYiIiIDU6JWAj0taq1qURMREZGBKVErAXV9ioiIyHAoUSuB3Mbs6voUERGRwShRK4GeMWqta+j27hLXRkRERMqVErUSmFgzkakTp9LZ3cnG7RW9JJyIiIhkSIlaiWicmoiIiAxFiVqJaJyaiIiIDEWJWomoRU1ERESGokStRJSoiYiIyFCUqJVIT9dnq7o+RUREpH9K1EpELWoiIiIyFCVqJaJETURERIaiRK1ElKiJiIjIUJSolUj+7gQiIiIi/VGiViJ71O+BYaxrXUdnd2epqyMiIiJlSIlaidRW1zKjfgaOs651XamrIyIiImVIiVoJqftTREREBqNErYQ0oUBEREQGo0SthJSoiYiIyGCUqJXQ7AYlaiIiIjIwJWol1DNGbZvGqImIiMiulKiVUE/XZ6ta1ERERGRXStRKSGPUREREZDBK1EpoVoO6PkVERGRgStRKSC1qIiIiMhglaiU0vX461VbNph2baO9sL3V1REREpMwoUSuhKqvS7gQiIiIyICVqJaZxaiIiIjIQJWolpnFqIiIiMhAlaiWmRE1EREQGokStxHq6PjVGTURERAooUSsxtaiJiIjIQJSolZgSNRERERlIZomamV1uZmvNbFle2e5mttDMnonX02K5mdkPzGy5mT1qZq/Je8wZ8f7PmNkZWdW3VLQ8h4iIiAwkyxa1nwMnFpR9Fljk7gcDi+IxwFuBg+PlbOASCIkdcCFwNHAUcGEuuRsr1KImIiIiA8ksUXP3u4CNBcUnAVfG21cC78orv8qD+4CpZjYHeAuw0N03uvsmYCG7Jn8VTYmaiIiIDMTcPbvgZvsBN7j7YfF4s7tPjbcN2OTuU83sBuDr7r44nlsEfAY4Dpjo7hfH8guA7e7+7X6e62xCaxxz5sxpWrBgQWY/V05bWxv19fWJYrg7r7/p9XR0d3D3iXfjHZ44ZqE06qmY2cdVTMUs97iKOT5jZhV3PMcsNG/evKXuPq/fk+6e2QXYD1iWd7y54PymeH0DcGxe+SJgHnA+8IW88guA84d63qamJh8Nzc3NqcR52Xdf5lyEP7vx2dRi5lPM9FVKXRVzfMbMKq5ijs+YWcUdzzELAc0+QE4z2rM+18QuTeL12li+Ctgn7357x7KByscUdX+KiIhIf0Y7UbseyM3cPAO4Lq/89Dj78xhgi7u/BNwCzDezaXESwfxYNqYoURMREZH+1GQV2Mz+hzDGbIaZvUCYvfl14BozOwtYCZwS734j8DZgOdAGfBjA3Tea2ZeBB+L9vuTuhRMUKl7+xuz7Vu1b4tqIiIhIucgsUXP3Uwc4dUI/93XgnAHiXA5cnmLVyk6fFrXdSlwZERERKRvamaAMqOtTRERE+qNErQz0JGqtStRERESklxK1MpA/Rk1EREQkR4laGVDXp4iIiPRHiVoZyG3Mvnrb6tzCviIiIiJK1MpBY10jDbUNtHe109rZWurqiIiISJlQolYmct2f69vXl7gmIiIiUi6UqJWJXKK2oX1DiWsiIiIi5UKJWplQoiYiIiKFlKiVidwSHRvbx9wOWSIiIlIkJWplQi1qIiIiUkiJWpnQZAIREREppEStTOTWUlPXp4iIiOQoUSsTuRa1e9fey9E/O5oLbr+Au1beRUdXR4lrJiIiIqVSU+oKSHDE7CM46ZCT+MOf/8CSVUtYsmoJF999MY11jRy/3/G8+YA3M//A+bx8+ssxs1JXV0REREaBErUyUVtdy/+9//9YfP9itu6+lYXPLuTWFbfyxLonWPDnBSz48wIA9tltH+YfOJ/5B87nhP1PYHr99BLXXERERLKiRK3MTKqZxLEHH8vbDn4bAC9sfYHbVtzGrc/eysIVC/nr1r9y2UOXcdlDl2EYTXs2Mf+A+bz5wDfzun1eR111XYl/AhEREUmLErUyt/due3Pmq8/kzFefSbd388jqR3qStrufv5vmF5tpfrGZry7+Kg21Dbxxvzcy/4DQ4vaKGa9QN6mIiEgFU6JWQaqsiiPmHMERc47gM8d+hradbdy18q6ebtJla5dx4zM3cuMzNwKw1+S9mH/gfGZ2zuT5+ueZ3TibWY2zmN04m/ra+hL/NCIiIjIUJWoVrL62nhMPOpETDzoRgBdbXuzTTbqqZRVXPHxFuPOyvo+dXDe5T+I2u2F23+N4mdkwU92pIiIiJaJEbQzZc/KenD73dE6fezrd3s1jax5j4YqF3Pv0vXRN6mL1ttU9l5aOFlo2tvDMxmeGjLv7pN37JG+zGmbRsbmD+7ruY7cJuw14mVgzUV2vIiIiCShRG6OqrIq5s+cyd/Zclk5YSlNTU885d2fzjs2saV3TJ3lbs20Nq1tX9ylb27qWjds3snH7Rp5Y90TfJ3lq8DrUVNX0SdymTJgyaGK35sU1/LXhr9RU1VBTVUNtVW3P7ZqqGmqra/s9l1+ef67KtEygiIhUNiVq45CZMW3SNKZNmsYrZrxi0Pt2dXexYfuGvsncttUs+8syGqY1sLV964CX9q72niRv2B5M+MMVmFA1gTmL57Dn5D3DpXHP3tt5l90m7KbWPxERKTtK1GRQ1VXVzGyYycyGmRw+6/Ce8qUT+7bS9ae9s52Wjha27NgyaEK3tX0rWzu28sKaF5g8ZTI7u3fS2d1JZ3cnO7t6b3d2dw54rrB8Z/fOUIfudp7b/BzPbX5u0LrW19b3Td4KEro5k0Oy11jXmPh3KiIiMlxK1CQzE2omMKFmAjPqZwzr/kuXDp38jUS3d3PPknuYc/AcXmx5cdBL685Wlm9czvKNyweNObluMnMmz6G2q5Y5T8zp7bqtG7hLt/BSW12b2s8oIiJjmxI1GbOqrIr6mnoO2v0gDtr9oAHv5+60dLTwUstLuyZx2/oet3S00LKhBYDHNz9eVL0m1kzsN4Gr3l7N3Ja57LXbXuw5eU/2mhyuZ9TPULesiMg4pURNxj0z60mWDplxyID3y03CWL1tNfc/cj977r/n0F26BZct7VvY0bmDHZ07WNu6dpfn+P3zv9+lrK66jjmNc3ZJ4HquY7m6ZUVExh4laiLDlD8Jo21aG00Hjryb1t3Z3rm9N3GL4/c279jMkieXUDO1hhe3vciqrat4seVFVrWsYvOOzazcspKVW1YOGnty3eRdkrkt67ZwS9st1FTVUG3VVFdVU23V4TjeHqis8DG5shWbV1C/rp6GugYa6xppqG2grrpOrX4iIhlQoiYyisyM+tp66mvrmd04u8+5/bbv1+8YvbadbX26X/OTuPzrlo4Wnlr/FE+tL1g35c8Z/CCL+x5WW3VI2uoaaKhtGPx2P2XPr3me9cvXU11VTZVV9SSG+berLR4PcTv3uJadLWzfuZ0JNRO0VIuIVCwlaiJlrr52eOPsNu/Y3Ju8xWRuxV9XMHPWTDq7O+nyLrq6u+jyrnAcb+fKB71PXtnGrRvxGmdbxzZad7ayrWMbnd2dbGnfwpb2LcX/oA8U/9AB3RKuaqpqmFAdJreM6LqgbP2a9dy47UbMDMOosqqe20NdD3Tfvz7/Vx6tepTa6lrqquuoraod8HZddR211bV9buffr7aqVi2bImOMEjWRMSC/W/awmYf1lKc9k3agmB1dHbR2tPYkbsO9vW1nuF63cR0Nkxvo8i66vbsnMRzO7a7ueFxwu31nO53eSXtXe8/SLa07W5P/ArJooXwsvVC5RZ9xqLol3ZZE73ZqFtb0tHRWWdUul1yLZp+yQe67o20H05ZNGzw5HWHCuvLFlfxl0l9S/dmfffFZnpv0XE/dzaz3dl4iPpKypzY/hb2UXtJvGGu3r2X1ttV9FgHPDWOoqapRIl+BlKiJSGJ11XXUTapj2qRpRT0+y4TS3dnZvZP2znbau9oTXa9ctZLZs2fj7jhOt3f33B7Odbd3708byhQAACAASURBVFK2dt1apk6fSkdXBzu7dobr7p19bufO5d/u7375aw4C0JXqrzS7mCNYE3vYUl48O7OYi4e+y4gtGvhUlVXtkrz1HFdVD5jgbW/bTv2D9alWc0fbDqY8MmXonWds+DvUrHphFfd03tMnMc5PjvsrGyqxfm7NczSR7vvTSChRE5ExzcxCIlldx2QmJ4o1Wi2UxcolpTu7dvLgQw9yxBFHpBI358GHHmTu3Lk9rZbd3t3vJdeyOeD5vFbRJ556gv0P2n/IJHU4SWtHVwcdXR1s2LiBadOK+9IwkI2bNjJ16tSeBD2XpPfczkvch1u2rXUbk+onFZX4D3Tf9o52qmqqeoYu5Cfvud977vc0YglGNgxocwYxnxj6LiMxoWoC577t3HSDjoASNRGRMSI/Ka2vqU99yZaGmgamTJySasy6tXU07V++ye9YiplLGAuTt/zj/hK8zu5OnnzqSV75ilemVk/HeeLJJzjo5QcNustMf+UDndvZtZPVa1azx8w9Bk+mR5hst25NYchEAkrURERExgEz65kZPYEJI3ps1eoqmvZKN6msXl1N08sqI/ktJc1ZFxERESlTFZOomdmJZva0mS03s8+Wuj4iIiIiWauIRM3MqoH/At4KHAqcamaHlrZWIiIiItmqiEQNOApY7u4r3L0D+DVwUonrJCIiIpIpc/dS12FIZnYycKK7fyQefwg42t0/nnefs4GzAebMmdO0YMGCzOvV1tZGfX2668ooZvnHzCquYipmucdVzPEZM6u44zlmoXnz5i1193n9nnT3sr8AJwM/yzv+EPCjge7f1NTko6G5uVkxx2HMrOIqpmKWe1zFHJ8xs4o7nmMWApp9gJymUro+VwH75B3vHctERERExqxKSdQeAA42s/3NrA54P3B9ieskIiIikqmKWPDW3TvN7OPALUA1cLm7P17iaomIiIhkqiISNQB3vxG4sdT1EBERERktFTHrc6TMbB2wchSeagawXjHHXcys4iqmYpZ7XMUcnzGzijueYxba19336O/EmEzURouZNftA02kVc8zGzCquYipmucdVzPEZM6u44znmSFTKZAIRERGRcUeJmoiIiEiZUqKWzKWKOS5jZhVXMRWz3OMq5viMmVXc8Rxz2DRGTURERKRMqUVNREREpEwpURMREREpU0rURERERMpUxexMICMT90R9eTx82t13lrI+IiJZMrPzBjvv7v85WnURSZMStREys1+4+4eGKisibmqJlZkdB1wJPAcYsI+ZneHudyWpY6Uws3rgU8DL3P2jZnYwcIi735BC7MOAQ4GJuTJ3vypBvInAWcCrCmL+Y4KYb+8n3peKjRdjVkTib2azgK8Ce7r7W83sUOC17n5ZCrF3I+890903Jo2ZJjP7KvBNd98cj6cBn3L3L5S2ZoGZLXL3E8zsG+7+mQyeYh5wJHB9PH4HsAR4JklQMzsc2I++f/vfJYz5TuAN8fCP7r4gSby8uDPp+3//fBpx8+K/wt2fSjNmWsxsEuE9/+mU4k0EPgYcCziwGLjE3XekEX9EddGsz5Exswfd/TV5x9XAY+5+aIKYx1GQWAFFJ1ZmthQ4LfeCNbOXA//j7k1FxFrs7seaWQvhxdpzCnB3363IOv6wIF4f7v6vxcSNsX8DLAVOd/fDYuJ2r7u/utiYMe6FwHGERO1G4K3AYnc/OUHM3wJPAacBXwI+ADzp7ucWGe8nQD1wPPAz4GRgibuflaCOx5Hi6zMv7sHA19g18T0gQcybgCuAz7v7XDOrAR5y979JEPOfgC8CO+h9zXrCer4cuASYFV+jhwPvdPeLE8R8yN2PKCjr835VRMxvAhcD24GbgcOBT7r7L4uI9QTwEeAywuvd8s+7+4PF1jPGvwt4u7u3xOPJwB/c/Q2DP3LQmJcTfubHge7eqib6IvU14Cjg6lh0KvCAu/97gpjvBL4D7AmsBfYlvI+8qtiYAzzP8+7+soQx9gZ+SG8CdDdwrru/kCDmO4BvA3Xuvr+ZvRr4kru/M0HMa4AWIPdaPw2Y6u7vLTZm0dxdl2FcgM8BrUAXsDVeWoANwNcSxl5KaPHJHb8cWJog3qPDKSvx7/OMwS4JYzfH64fyyh5Joc6PEcZ1PhKPZwELE8Z8KP/vA9QC9yX92+ddNwJ3J6xjqq/PvDiLgROARwkfLBcR3lyTxHygn7/9wwljPgPMSPrzFsT8I+HDOr+eyxLGfBSYkHc8CXg8YcyH4/W7CQnWlGL/lwhfGm6K75u3A3fkXW5P4Xf6dMHPP4HQ+psk5hNp/t3z/k5VecfVSd+fgUeA6XnvJ8cDlxUZ6wcDXH4IbE3h518IfJjQQlkDnJnC++jS+NrM/396LO2/fRavh+Fc1PU5TO7+NTP7OvCMux+Ucvhaz2uudfc/m1ltgnhLzexn9H4T+ADQnKSCOWk1rbv7lWnUZwAdsRncAczsQKA9hbjb3b3bzDpjN9haQutSErkuxM2xW3U1MDNJHeN1m5ntSfgiMSdBPEj/9Zkzyd0XmZm5+0rgotga/B8JYraa2XR6//bHAFsS1vNZoC1hjEL17r7ErE+jUmfCmL8EFpnZFfH4w4SW0CRynxFvB37r7lsK6jxs7n4tcK2ZXeDuX05Yr/5cBSwxs9/H43cBP08Y809mdqi7P5EwTqGpQK7rfEoK8Xa6+wYzqzKzKne/w8y+V2SsDxOGjvT3nnlq8VXssYe7X5F3/HMz+0TCmDv7eW0m7S580MyOcff7AMzsaFL6HB0pJWoj4O5uZveY2ZHu/kCKodNOrP4fcA6Q6z68G/hxgngDNq0TxkIVE+977v4JM1tAP/9QnqDJmtAyczNhbN7VwOsJbz5JNZvZVOC/Cd/gtgF/Shjz0jiW6AuEsTWNwAUJ4t0Q6/gt4EHC7/ZnCevYnFHi325mVcAzZvZxYBXh50/iPMLv8UAzuwfYg9CSk8TngHvN7H7yPrw8Qfc8sD5+gcgllCcDLyWqZWjtvwTIdfF/2d1vSRjzBjN7ivAF4J/NbA9CF3ASxwF9ErXc+LUkQd39K7Hr+29j0Yfd/aEkMQnJ35/MbDXhb58b8nF4gphfAx4ysztivDcAn01Yz81m1gjcBVxtZmsJPUDFeIDQuntv4Qkzu6j4KvbYYGYfBP4nHp9K+EKZxONmdhpQHYdU/CuwS/1HqInwf59rjHgZ8LSZPUby18CIaIzaCMU3rYOAlYR/hMT/uGY2gZBYHRuL7gZ+7O4jbgWKY+Yed/dXFFufAeI+ArwJuM3djzCz44EPepFjn8ysyd2Xmtkb+zvv7n9MUF1iq8oxhL/Pfe6+Pkm8fuLvB+zm7o8miFEFnOzu16RVr4L4E4CJ7p6oRSnN12dB3CMJyf5Uwgf3bsC3ct9gE8StAQ4h/O0TT3wwsyWEbtrH6B2nlKhV2MwOIGxL8zpgE/AX4AOxZbHYmBcCpxBaan5DaAFbU2y8vLi7A1vcvSuO99zN3VcXEWci0EDo9jyO3jFquwE3p/2elQYzW05I/gv/9kX/nWLcOYSJDxDGkI7491kQr4GQTFcRvkhNAa529xEnQPHvvcPd025FzsXfl9CN+lrCF5V7gX8ttncmxqwHPg/MJ7yubiF8USn6S0Ws54CSvgZGVBclaiMz0B+v2D9aFomVmV0H/EuSF34/MZvdfV5M2I6IXYCPuPvcFJ9jGrBPkuQnxtnl23mSb+xmNuhgbE8wCDr3ey328Xlx3uTut5vZe/o77wlnqaUtvu6/4e7npxRvsJ/fCcnLYnfvKiL2LoP0k8j/2eMHbJXHAfApxT8ceB/wD8AL7v53CWLVAv9M3gxF4CfFJL9mdi7wCUKr/Cril1zCmLVL3f2/iq1nVszsT+7+2pRjLgB+BVzv7sW2euXHqyZ8gT4+ceUyFut6lbt/oNR1GUps8X7B3dstTKg6nFD3zaNdF3V9jlDaWXT8lvq0mb0sxcRqGqEpeAl5zd8JuxNzTet3k7xpvYeZ3Qm8k/BaXAqsNbN73H3QNZEGiDWRMONxRkz68r+x75Wgmt8Z5JwTWhqLdZuZnU9oAcn/W4106Yc3EFoq3kE/s3OBohM1M3s9oTt5X/ouUVD0rMf4uj926HsO2xvp/fn7M53QvfzmImLfZGZnAwvo2/VZ1PIc+T97Gh/U/VhLGOu4gWTjHSF0pdbSO3TiQ7HsIyMN5O7fB75vZv8BfM/dt5rZBcBrSD6EICsPmdmv2PVvn+SLz7cJifTXzewB4NfADcW2/sTXU7eZTUnaeg5htqO7n5Lr4ss/RcLeo1jXfc2szt07Uqhrv0Nn8p4vyWfe/wLzzOwgQuv3dYQE+20JYhZFLWplwMK08iMIa/4kTqyy6E40s88TBuauBj5Igqb1grgPxa7UjxBa0y40s0eLeTMY4Bs7hBm6/+3uP0pS1yyY2V/of4zeiJIgM/tUjGN51+Rie4LFPmN3/ycJiXRPi1QKf/tLCAn0b+n7us+k9c/MLiumqz7+jQp5kkQ1i5/dzD5G6PrcI8a9Jukg+P5azZO2pOf+v2Oy+mVC4vIf7n50krpmwXonZuRzT7A8R17sasKXvI8CJ3qRSx3FWNcRPkMW0vf1NOJxlGY2x91fSrv3KC/+VcArCWNJ8+s64veovM+69wCz6R1Heyqwxt0/maCeD7r7a8zs3wgTyX6Yduv6cKlFrTwkGTy+i6TjuwZQA9xK7/iX3yT9oM7FjeM1TiGMMSha3jf2f3H3H6ZQtz7M7PQBnrfoBW8Ja4jlL6p4N/CTIuLkBuEfQhj7ch0hWcst+pnEFne/KWGM/kwktPrkt0gmbf2bAlxI3666L7n7lmLHU7r7/sXWZxCp/+yEGcifcPeHk1SsQJeZHejuz0LP2LoRdx8XxozXbyd8gfqDmRW9flyW3D2NSUi7sDAr/R2ElrXXkHx27u/Y9bVTVCuMu78Ur1fGZO1gd78t1jmNnOHZeKkCJicJlPusM7PvFAwhWWBmSSc87TSzU4HT6W2pT2O2+4ipRW0MsrAkwQ8J31rqCOv0tCb5xpYXO7XxLzHeewmJ6mJ3/1j8IPiWu/9DwrivY9fVxJMkVLlFenMmEtYAe9CTLXh7DaHFL7f45WnAFHc/pch4WSz6+XXCa+h39O3+SbRAaRbM7H+BZfR+8H0ImOvu/Y7dG2bMwnFadwI/TTpJoRKY2QmEBYRXEBL/fQmzKe9IEPMGQov3mwlJynbCgPrUxrsmZWb/5u7ftAEW5i6mpSov9jWENfRuJnzp/aO7dw/+qCFjnhu/qA5aNsKYHwXOBnZ39wMtzKb8SbFjffuJ3wjg7ttSiPUk4X1vRTzeH7jR3V+ZIOahhBUU/uTu/xNjnuLu30ha3xHXRYla6aWdWMVvEu8ndH/MI3wjeLm7fy6Fus4G3hvjT04yXiErZvYL4EDgYXq/vXuSN9cBnmcq8Gt3PzFBjCe8YFeL/spGEO9p4HCPMzItzNh81N0PSVDH/j6U3d2TjM3LX6H89bEojRXKH/aCHSj6KxthzJ8RvknnJ39d7j7icVp5MQ8Avk+YmeyEMVqfcPf+ullLKr6Gcq+fpz35bN964ETCgqTPxBb1v3H3WxNWNTVm9g53X2BmZ/R33pPN+H0LYfB/0pbJ/Ji77ECRtJvOzB4mJJT35+KY2WOeYJePGOMw4BfA7rFoPWEXmccTxDyRMI4s/wvF2UlfU1YmW+ep67M8/Ih+EqskAd19uZlVxzeDK8zsIcJ6UEXpZ/zLR5OOf4lxU9/rkvA7PNSz/xbSCiTtFkt7UcVUF/2M42iud/fvJqjTQK4gDM7NbcnywVhWzID/nO1mdqy7L4aeiRDbh3jMUI4saO253cLs5yR+BfwXYcV/CP//vwbKYpyWDTB7GDjIzBKNpfOw7MPv8o5fIvkacqnyuPdmkoRsELcD55hZGjNpTyW0wu9vZtfnnZpM74K6xWp39w6Li8haWPYmjffUS4Hzcq2yFmZU/jdhqZqiuPvNscUvt3rCUyl8oTiOMtkzW4lamUg5sWqL3wQetrBX30uE8QBJZDH+BcI3q6eAt5C312XCmMsIA0tTffMvmGFURRhflnQNtFQXVfSUF/30MEvrVCCLRC2LFcr/GbgyjlWDsEZZv60iI5DFOK16d/9F3vEvzezTCWOmKTcmZybhA3QR4cPqeMK6V2W13EtWLCzw+xl23Y82SWtyajNpCX+Ll4AZ9J2d3kLYqiqJP5rZvwOTzOzNhLG0aWwe35Dfde7ud1pYpqZocXjCP5E3PMHMkg5P+A4w3wv2zCa8Z48qdX2WgTiu6O8IK8ivJvzjnVnsmI04AHQNoRv1k4QZmj929+Xp1Dg91jvrMzcTrJawN+UxCWLeQVidfQl9x1QlmapdOJu2E1iZpJsuxiybRRUHYmbfJXywFC4hknQT7UWEFrT8Fco/nGQMTOymO5nQ9T2VsH2Uu/uXEsTMH6cFYexjUeO0LCwmCuHDfxOhFc0J4z6npTE8IU1mdith792X4vEc4Ofu/pbS1mx0xJ//N8D5hPFKZwDr3P0zCWKmPpM2CxYW5D6LvovI/ixpT0Vs7X+Q8CUdQkt6k7u/e+BHDRkzi+EJu6w+0F/ZaFCiVgYqKbFKm5ktcfejYrL6MUKiusSTLX2QyW4H41WGY9SyWKH8ZmAz4YMgfymRwdbCGyrmRMLehyfE2A8A3/Ui1r2y3uVY+tsw05O87rNgZk/mD8iOH96PJxmkXUnMbKm7N+V/QJvZA+5+5FCPHSTmg8B7C1pory0cYzbCmO8BvkFoAbV48WLHOWfJwhqXX6Tv2NSLPMFCslkkvxaWZumi79Z51QmH5RRFXZ9lIK/VZAfhBZyIZbBAaYbS3usSd/+j9Z1WXk+YoFEUM2uh/7EZZftmmCbPaMXz+LpP1MrZj72TTO4YwFWEmbm5/SlPI7QGvHfARwzAs1nqI0uLzOwWels93wfcVsL6jLZc19lLZvZ24EV6B8EX69PAHWbWp4U2YcxvAu9w96TDRnpYSms89uNAwlCaKsLn0wmEZWqStFRlMTwh9T2zi6VErQxkkFhdRj8LlJab+O18q7tvImwmnEoiaXnTyglvCnsR1iYrqkvN3ROt9VPpzGwW8FVgT3d/q4Vp669198sSxv1BP8VbgGZ3v67IsPea2d+4+2MJqlboMO87C/cOM0u6kGxFLPnh7h83s3fTW89L3f33gz1mjLk4jnf8FKH1dzfCe2sS9wA/pbeF9haS78ywJs0kLcpfl2wi4YtJ0iQVwlJE5xPGEidaliRPqslvnET1iIetHYteLDwt6vosA5byyu9mdr+X4Srf/bGU9rosiJnJtPLxKk5OuAL4vLvPjbO/Hkphmv6lhFlav41F/0DYnHw6sMLdhz2xwHq3u6kBDiaMJ2uH5NvemNkvgR8VzMw9x937XQB5mDFTH1OTlZioH0X4/S5x97UlrlJFs/7XTpzq7iNuoc2L+X3CBKr/I72trvp7nqXunmgwvZktdvc0t49LdXhCXszU98wullrUykPaK7/fYWbfogIWKCW9vS7zZTWtfLya4e7XmNnnANy908zSaKk9HHh9nOmMhW2V7ibs0jDSFrG/T6E+feQlf7X0zsx1Qsv3UwnDZ7HkR+rM7BTgW4QWPwN+aGafdvdrS1qxUWK96929ltD68yfgkx4XVi1S6i20hJa+NsLA/5yku3zkj5mrIrSwpZEzXBi/qCwivaQyteEJebLYM7soStTKQ9qJVa41Lb+lKunm4Vl5H6FuHysoT9INmtW08vGq1cymE5NdCws0J978mfBG2JgXq4GwCnqXmY1oDaSMZsemnvzlyWJMTRY+T0gq10LPchW3AeMiUaP/9e7+h2Tr3aW9dmJWW13lT8DpJKwnVtSOKQU+TGhJr6W36zPp9mlZJL+pbu2YhBK18pBqYpXV4O+MpLXXZb7PEqaVP0ZYW+dGwtInUpxPESZ6HGhm9xAWPS5626w83ySs9XcnobXmDcBX45pKJR+wnvHSKOeT/oDyLFQVdHVuIPmajJUki/XuUl07EbLZ5SPDz5EjPcFOKQPIIvktm1UCNEZtDLJBNqYuXa36N8B4jaL3upRsxO7jQwgJVWpbqZjZnoTxWU8SWtde8BKs/D3aLOxxewshQXsXoWvt8+U2PMHCgtlz6Tvr89Ek64hVEjP7Bv2sd0foDi5qiEYWayea2UJC61/+2mQfcPeid/kws/MGO+/uRQ2yj8tefMtT2NkmL+aThPenPskvoSWw2OQ3sz2zR1wXJWqll3ZiZRlsTJ0VS3Gvy7wxRf0qxUKFY4GZPUr4oPpNrqsupbgfAc4F9ibsy3oMYQPkcuyiT5X1LvB8LGFczbeB/yi3SUAxUbmf0OINoaXmmHGUqA2292rZrHtn2exx+yvgSEJrOoTdKpYAzwC4e1FLScWk6kDCxKG0Jvxkkfxmtmf2iOuiRK300k6ssvinzUqaM+ry/lnPidf53y7d3T+btL7jUfy9vi9eugkTP65JOhsqJtZHAve5+6vN7BXAV8vxC0XarHdHjq8RNif/lSXcRDsL1v9m3yVZnV0GZtns8nEX8HZ3b4nHk4E/uPsbBn/kkHH7TaoyHmowYrkVCazvYscl+R/VGLXycKC7/0Pe8RfjEhPFymJj6lRlMaMu949uZm8u+Gf6jIXVwJWoFSH+Xr8JfNPCxscXEFZBL3oR4WiHu+8wM8xsgrs/ZWZpj10pV6vM7KeEDei/YWHrq7IZ+2Vm/0wYO3pAbFHNmUxYB2xciOtpvZ3QRZ2/xmXJ19Yq8I+Ebrrv0rvLx5kJY84COvKOO2JZIuWWkA0iiz2zi6JErTyknVjlb0xtwEaS/9OmLcsZdWZmr3f3e+LB6yijD8FKVNCq1gX8WwphXzCzqYS1nxaa2SagUt7EkzoFOBH4trtvtrCHZjltyv4r4Cbga/T9gtOScOmcSrOAsGPMY6S3OGsWvkTYk3UTkNtT9tuEBK5YVwFLLOzNacBJwM8T1rOSfIjwufFxwjqn+xDWehx16vosA2b2akK3Z5/Eyt0TratkZrsBuPvWxJWsIGbWBFxO7+9zE/CP5TZQu1KY2f2Els/fEsapJVlDaqDneCPh73Wzu3cMdX+R0VAp3bz9dcml0U0X11L7W+KMfHd/KEk8KY5a1MqAuz8MzE0rsTKzcwnjFVqA/47/bJ9191sTV7YCuPtSwu9zSjwuu9muFeZ0d386yycop6nwInluMrP5FfDeWWVm0wpa1NL4fO8itCQ65d2imDoroz2zlaiVgQwSq3909++b2VsI2/F8iDCwvtzfbBIxsw+6+y8Lp5Vb3KGgDMeVVIrVZvafVMByLyIpuw/4vYV9iXfSO0Nx1JdoGMJ3gD+ZWW47tvcCX0kSMH4ufRT4X8LP/Uszu9Tdf5ioppWjbPbMVqJWHtJOrCxevw24yt0ft1y2MrY1xOtxvYl6Bi4nzErOrW33IcIXizE/O1PGvf8krHH3mJfxOCF3vyouJ5Fb2uY9KaxTdhZwtLu3Qs9SLX8iTFoYD9Le2rFoStTKQ9qJ1VIzuxXYH/hcnFY95put3f2n8bqo9X1kQGnPShapFH8FlpVzkpYTE7PUFpElfC7ltyR10ftZNR6UzZ7ZStTKQ9qJ1VnAq4EV7t5mYZ/GctyeJhNxKvXFhJmzNxM2//6ku/+ypBWrXGW/3ItIRlYAd5rZTfT9sB4PwyiuAO6Psz4h7KBxWQnrM9rKZs9szfosA3H8Qy6x2hwTq73c/dEhHloY5zWDnR8vsx5zi/ua2bsJy4CcB9zl7nNLXLWKZGZzCVP1p8SiTYSlAEb0+hSpNGZ2YX/l46XVPs6g79k/VLM+S0OJWgmlnViZ2R2Dhxv7W/MAmNkydz/MzH4GXOvuN5vZI0rUipM3OaMxXm8DtgBL44xlERmjzGwmMDF3nHRHkkphZbRnthK1ElJilQ0z+zqhmX47cBQwFbih3PZRrBRxz795hD3/jNBK+Shhtfbfuvs3S1c7kfSZ2ffc/RNmtoB+9g9293eWoFqjyszeSZhNuiewlrDR+VPu/qqSVmyUpL21Y6K6KFEbe8yslrA7Qe6bwJ3AT919Z8kqNcriOkJb3L3LzBqAye6+utT1qkRxz7+3ufu2eNwI/IGwsv5Sdz+0lPUTSZuZNbn7UjM7H3ig4PRkd7+hFPUaTWb2CGE81m1xX9rjgQ+6+1klrtqosDLaM1vb6pQBM6s1s381s2vj5eMx2SrWJUAT8ON4aYpl44KZ1RP2Kcz9zHvSd0CojMxM8gZSE9aTmuXu2wvKRcaEuGg2wGnABnf/Y1yUeU/CXrfjwU5330BYTLfK3e9gfL2PbjezY3MHpZxEpVmf5eESwhY9P47HH4plHyky3pEF47Fuj9+OxosrCIsUvi4eryJsfzTmvwVn5GrC7K/r4vE7gF/Flso0lwMQKTcnA9ea2WmErZROB+aXtkqjZnNsPb8LuNrM1hLGp44XZbNnthK18pB2YtVlZge6+7MAZnYAJV5ZeZQd6O7vM7NTAeISJeNp/Z9UufuX4/IEudlf/8/dm+PtD5SoWiKZc/cVZvZ+4P+A54H5sSV5PHgEaCOszv8BwqzvxkEfMYakvbVjEkrUykPaidWnCYv1rSB8E9iXcbSOGtBhZpOIg4DN7EDURZdITMyah7yjyBhgZo/RdxLB7kA1oWWZStioPQXHu3s3YU3PKyFsUl/aKo2ectozW4laechPrCDMpis6sXL3RWZ2MHBILHra3cdTonIhYaHbfczsakJL0JklrZGIVJK/L3UFSsXM/pkwxvfAgsRsMnBPaWpVEmWzZ7YStfJwD/BT4ARgM3ALYU+1opjZRMI/2rGEb4V3m9lP3H1HCnUta3Hx4GmEfSiPIbQonuvu60taMRGpGO6+stR1KKFfATcBXwM+m1fe4u4bS1OlkiibPbO1PEcZMLNrgK2EQdsQZhpNdff3JojXAuS2TEoUr9KYLIdIXAAABRRJREFUWbO7j6fZSSIikiIzuwLYi7C141xC1/ed7t406nVRolZ6ZvZE4VpU/ZWVKl6liQvergd+A7TmysfZt0ERESlSWls7pkFdn+XhQTM7xt3vAzCzo0k2cDvteJXmfYQu348VlB9QgrqIiEiF6GdrxwNKvWiAWtRKKG9mUS1h4P/z8XhfwlYdI2oBSztepYozPvuM0QN+Mo6m1YuISBHKcWtHJWolZGb7DnZ+pANa045XqQYY8zfF3U8pXa1ERERGTonaGGNm1cDj7v6KUtelVMb7GD0REUmmnPbM1l6fY4y7dwFPm9nLSl2XEnrQzI7JHYzDMXoiIpJM2eyZrRa1McjM7gKOAJbQd9bjO0tWqVFkZk/SO0YP4GXA00AnYYzBeFhVXEREimRmjxRs7dhv2WjQrM+x6YJSV6DETix1BUREpKKVzZ7ZalETERERyWNmJxD2+uyztaO7DzYrNBMaozaGmNnieN1iZlvzLi1mtrXU9RMREakQua0du4GN8XbRWzsmoRY1ERERkTxpb+2YqC5K1MYuM5sJTMwdu/vzg9xdREREKK9lntT1OQaZ2TvN7BngL8AfgeeAm0paKRERkcpRNss8adbn2PRl4BjgNnc/wsyOBz5Y4jqJiIiUtYKtGO81sz5bMZaiTkrUxqad7r7BzKrMrMrd7zCz75W6UiIiImXu70tdgUJK1MamzWbWSNiM/GozW0vewrciIiKyq3LcE1uTCcYgM6sHdgBG6PLcDbja3TeWtGIiIiIyIkrUxhAzW+zux5pZC6FPHUKyBr1rwXzL3X9ckgqKiIjIiChRG0fMbDpwr7sfUuq6iIiIyNCUqI0zZjbH3V8qdT1ERERkaErURERERMqUFrwVERERKVNK1ERERETKlBI1ERnTzOzzZva4mT1qZg/HrWCyeq47zWxeVvFFZPzRgrciMmaZ2WsJK42/xt3bzWwGUFfiaomIDJta1ERkLJsDrHf3dgB3X+/uL5rZf5jZA2a2zMwuNTODnhax75pZs5k9aWZHmtnvzOwZM7s43mc/M3vKzK6O97k2LjLdh5nNN7M/mdmDZvbbuFsIZvZ1M3sitvB9exR/FyJSgZSoichYdiuwj5n92cx+bGZvjOU/cvcj3f0wYBJ99/frcPd5wE+A64BzgMOAM+NahACHAD9291cCW4GP5T9pbLn7AvB37v4aoBk4Lz7+3cCr3P1w4OIMfmYRGUOUqInImOXu24Am4GxgHfAbMzsTON7M7jezx4A3Aa/Ke9j18fox4HF3fym2yK0A9onn/uru98TbvwSOLXjqY4BDgXvM7GHgDGBfYAthe7fLzOw9QFtqP6yIjEkaoyYiY5q7dwF3AnfGxOyfgMOBee7+VzO7CJiY95D2eN2ddzt3nHvPLFyAsvDYgIXufmphfczsKOAE4GTg44REUUSkX2pRE5Exy8wOMbOD84peDTwdb6+P48ZOLiL0y+JEBYDTgMUF5+8DXm9mB8V6NJjZy+PzTXH3G4FPAnOLeG4RGUfUoiYiY1kj8EMzmwp0AssJ3aCb/397d4yCUAwEAXS20mN4Pztr72Fnay14C1sRvIFg6QG+hSls/SAs+F6ZQNIOs4QkuSS5JznPOPeWZF1V+yTXJLvPzWmaHmPEeqiqxVjeJnkmOVbVMu/WbTPjbuCP+EIK4AtVtUpyGg8RAH7K6BMAoCmNGgBAUxo1AICmBDUAgKYENQCApgQ1AICmBDUAgKZeYFs+HpQ41RQAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = não:\\n\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='não'])" + ], "metadata": { - "id": "f4DmJ2b6vEn7" + "id": "xxyZtxgw2POm", + "outputId": "14674b12-8a47-409e-f9db-78ab1ef1c123", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + } }, - "outputs": [], - "source": [ - "#x_new_trend" + "execution_count": 277, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gráfico de frequência de Comportamento agressivo = não:\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "def nuvem_palavras(Agressivo):\n", + " Agressivotexto = ' '.join(texto[texto['Comportamento agressivo?']==Agressivo]['tweet_text'])\n", + " wordcloud = WordCloud(\n", + " width = 3000,\n", + " height = 2000,\n", + " background_color = 'black',\n", + " colormap=\"hsv\",\n", + " stopwords = STOPWORDS).generate(str(Agressivotexto))\n", + "\n", + " fig = plt.figure(\n", + " figsize = (8, 4),\n", + " facecolor = 'k',\n", + " edgecolor = 'k',)\n", + " plt.imshow(wordcloud, interpolation = 'bilinear')\n", + " plt.axis('off')\n", + " plt.tight_layout(pad=0)" + ], "metadata": { - "id": "ijck93gzvEn7" + "id": "Uh33_5yC2gG_" }, - "outputs": [], - "source": [ - "#confusion_matrix(y_test, y_pred)" - ] + "execution_count": 278, + "outputs": [] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "print(\"Nuvem de palavras para agressivo sim:\\n\")\n", + "nuvem_palavras('sim')" + ], "metadata": { - "id": "OSdmUudLvEn7" + "id": "lC93YnyQ3PhL", + "outputId": "cbd73cb6-3d3f-4eba-d62b-8ef16a54a222", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } }, - "outputs": [], - "source": [ - "y_pred" + "execution_count": 279, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Nuvem de palavras para agressivo sim:\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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mhfh6kGSvAt3T+FMTz3ZwRWmw4q5rthfTzY6YmBX61InxWUHWrE9xYDz4/D5VG9qbG89Ya/+DcK0ruydpqFXT5/NE5r82rVLSkpJatezT4JZU5r92bZK7+Ebq1anXO5wtYomFVlmhwlA58rqJj0CsiYz3bTDrjZiYB9ztPnd1Wx1NvSyV3lhlufv9s9uPtckGN7rOz/3e4Y7ymlfkyXOWt9tum7+6RqMG0KbVP9zoXd7vLG/zR7+zzdZB9LAvWrVKSJhqhkpDbbZJQsJWW3a4rjOdKynpT/6gJiOvdujwgHs85TEnOc0Y46y0vPu8RV51u5u0ZcZ0u20e95DjnGS4kd3El5S02Gvd50VE1Kn1lEed6kzlKgYc64fc4z7/7JbN+4/1ZNMc6Rj/9A9PeKTbr1dli2v8PiPbnuNFzw4uynigoBURZl8QwjAf+hIt1Rz9Td300d4Qwim7AmLiBRk5E5PPZNKp3Pup4Iyb+x8MP0iiTffqONGj2OpoCK/e3WnctGMXmzb373ewBvv0OrJDEQTimnME46Yy+/AgYfYpkMHG1Rx9WpA5Zx8WyHPVIs75UDh20LGsXEhyX7CeNzCqMy7ms07QolO1JjERERHZYt7nMK/Y6AXr5MpSJFepfAkpt1tgswblCmzXvEvS60JSs2Q/P0aq14q4d8pCWkKH2n12nQs7ubiQd+cHgnmqPUiQg0GHEDjzvXq+UxIiRh9sD9GYh2RzdE4IojknP0iLTw9STz04O/j2tiT5j0LubA0SZVWmX2fnBeI7M68vaY+JhdSMo3JCxOrbMuWe2bdxDztFg3rLLP7XNLYPEBMzwWQHO8xEUwxRqkChyabaZuvrTi9ISnjV/D5S286wxaZuIuvCKsu1azPRFBERxYYYb6IN1u5AbI0aLLfEGc41zIi9Ir7XvOImf/UO7zbPwe52h3vdYZEF2nr5yPLkmWKarbbYaF2fOjq0W+gVpzjTGOP7EN8qK3a4xmbNIkKgTm+UqzDPoWaZo9Iw+fJNN0tEZEApPCnpFS91k95AGG+CIiVe8/IOwSxrrVKj2kwHyJbTxye4x8gupLO5x2obeXiQO2HTM8w4jzFHBdNrypnBJ0iIMkm0hXOLxzD2+GDK/QuQTvP8Q2zbwlP3BovuuYdY9GKI0DzxbbzyFKsWh0CX+swUvHoxWzcEknv6ft58biizcTUrFvL4XeHc7Vu4/6Z91983jPhyZZlmmF951HDFsjMeqDPNNtVQT1ppgnLr1XrJevXaPGmVXHE13ST2r4+Tr8imrjPIiTlZ3NYR/Gj90RXUcmsL+RGOzaEmxYsdlGbuw9pealQEMwqpag/1/62FRZnPH23n0oaQYhAXglNmxUMwzY0tIfgkLxJ8d4+2h1FpT3N9c8i5SwvWYhc/3dMaLMejc7ihhb9lLMklnXyrjlPyguV3fTP3twWChIpMuzVJbm0NKRNd7T7QRmMqRHle1xzOqUuF6+jcR19TiC7cN7lybzSyZLnAh33CFyQlLLXYFpvUqtln1mdKug9h7ArtA0ROBos1KV8+ZGI4c7Rq3WHiTktr0SwuLkfuXvW3WZNv+4rHPeSd3u9CH3GhizzgHle4zPKMEhQTlytPu/YdJFto0iQrIzX3RquWQVlSB5jre35qsqlWWGqtNRrUZUhz4MVIl4W/K+QpEBHRPEAwXIcOHdrlKxhcnmP9GmK9xrl1W/D5pZMsuiFYeW+5PGiOS28NZEYIu1zwZ970pWD9bZkfcgxSCVbezYjDOOmntNay5v5MG/+aefT5R8LfpzMGfnXm702/z2RX9OrGusx6ZvWSnmMP3Lxjuafv45n7+x7bF9inxPe4lTZkrKkmHf7qBaeYYZ1af/a8TkkpaWvVOMokNZptUu8qTznPQd7vMKtUW2mb1ap17gM1N4KZRWRHWd3MmDw6UlR3Mj6P1S1U5oRy61qZXsiLdaHcoSU83kZxAZMTQV8eEmdtK0eVsrmdxY08H+MfLeHfacwrZEo+z9fTlmRkLsuaKcsmHg2kuiKbDZ1Mzic/i1fbGZPNWGxPcn+EFc2MzgsBNNs7mF7MghZqMnPFdb2U2r+09Pyka1Nc3hiiUntPhUnclYnGZMefw/zO8BoI9/eaE/7Sq921r18t/rdECKz4ppWW+6KPW2OlTp3y5JtjnhJD/qX9yZcvJqtPYEW+AnFxjRqkpbVr06JZoSJZ4n18blFRxYbo0L5DFOieoFmT293sAXebbrbzvMc7XWCoYS7yXvVqderQqNEww+XI2aGOIUp16uwTgDJYhKCZr5rtQF/0cfe7S6sWKSkfcrFjnLDX19agTkpywO82T55ceRo1DC645bV+SWrrn2Tjs6Q6qV7MXRcFGbOjOfjzuvL2kh3M/x2Lrg9s0NEYpM5kO51N3PdJsouCzzDREvL8Xk80SEk2M8pYUsOQHI4dxfAC1jXy2EY29btXYhHmVFDbzoZGDqjgTcOli7JZ38gjG9jc0rf+mWXMLqc8T7qlk8U1PFdFfVhA7kB6o8oYVxE+WLiBhlZ7in1KfI/2kiXS0u6xyH0WS/WaYm/z6g7nter0W4+LinSXXWm7la8ztJpgmR1ZyvpWyuKMzeOuKk4byrAcyhooz6amI5Dg6FyWxzm6jMYEUwsYlRvIJpkO9RRmhe+3rpM3lXJgSSCpmzNaeFTYbeXYskA861uZV0xThseH5nBgcSDF2UXUJphbHOqbVsiqZg4vJS/G1MLQ3zeVUt/JUWXcMcigu53d7v/DN2QZFNKZ/2IZEf1fjfEmKlfpd67oI88OUWqo4a9P6toLjDZOmfI+ofozzBYXtzTTv3p1lljoMEcaZUyffpcqM90s661Vpb9TZ8/RosVLnvOaV+QrdJqzjDZGvVptWi0w3wE+aKIpqnr5APPkm+tgW1VZZ/Uet1ukyDQzrbbCQ+7tjsSNippkqtcTjrXSctW2O8ihsmX3seynmK5MuVfNH1xKQ6rfCjOd7OvA6mwJr96fd79P0dbLXdP7vERbX0de+nWqDwdUcP0pXLOYE0ZTkReGcFg+r2znEw+zsJcfvjDO5ceGz16r5gvzyI6FiTgniw/ezz/XhLK5MX54JOdOoj1FUwdF2RRnc+dqPvsY1f1+R9lZvO0whpbQ0EJt814R3xu+V2dqD6bZPSm7JyiIURIPxLG+laoONrSG9yuaWdcSrLIhcYqyqMymJUlhjG0dwZJ7tYFNbYEcW5NsaQ+yaHUnG9tY1Gtx2p4K9TQnqe4IhNucZEROeLUmw6ssO5QtiLG1PfSlrpNx+TQk6EyFfm5pDxZflwy7M6Rxd2tPPuD/ZrRqUavaeBNNMFm2bDly5drNBqj7CPXqdegwyVSFisTElKvwQR8zzvgBz4mKysoIjhFBLo2LZ6Sx10fe44z3ThcYolS2HDPM9m4X2miDZz2BIMfd6Dp58n3UZww3UrZspcq830WmmOYWN/QJKIqIiMmSLVs081+2HFmydlhwTDXdPIdk+hC+kaGGGWqYJk3dJJSScpu/a9HsYp813kTZchQp9g7vcZgj3eUfNlpvT9GuXYN6ZSqMMkZMTK48b3aK0539OkaYNVa62+2Oc5K3eodCRbLlmGyaj/mMWjX+6bY3ZPu8NwolQ8kr2kWBWIQhuVw0ixuXc8ptnHgrX32SA8r55mFBsuqNvCzOnMA7JvO1pzjp1nDOe+7h2V6BTu1J7l3H5x7n5EyZU27j9lW8fTInjhm4T1vrWbSBtk5y924P3X/7Lct2h7QgTT5eE3xu8cxv9bGaQHRNiWDNLWikLcXtVYH0VjaHBUlTgvxYJo8lFfxZa1uDlZ2fFazClc2ByLqwpCmQYVsyyJqFmXJr4qFDzUnu3RZILB4NSfGtGV/d6pYgqWZHac7Iq2k8V0dJViDEXeHWPV/8/FuiUYNb/d3XfMc1/m6VFbJle8VLLnPJXiWw7wle87JHPeAc5xltjGrbjDVRiyaPeKA7ob0LhznSBT6sSLGRRsuR650uMNchmjV5zlP+5MpdBlfsCg+5zxnOcYoz1ak12TQlSlzqW1ZZ0V3uYff7pf90sc861JtssE6FShNMdou/uc7V3RN3sRKf8iXjTVSsxEwHiIv7lWvUqbHdNr/yE+utBUc53ldcYoP1tqkSETHWeOUq/dpPbegVyPKyF13mEl92ib+502orFSsxxXRPeMRv/HSvvsMmjW50nW/5gd/7i8VeU2KIkUZ70D3Odt5ejS9h4XCFHxlhlEv9zIU+olmTiabIke0HLrFoAEVrn6ByKhOP6wmf3PASG17o/rhkKDOOoLmB6o1MmMPa1ygbEQzCqlVMnEeig6Y6ajYRz2b4pFBuyFDGzmbJUwybSEkF8+/vldM7fxtXLaI5s/L+U8YCPGE0U4YEC683ynL4+XzuXttzbFm/DT3T+MeqvseqWvjDQs6ZxKwBtsfrSHDXyxTkMGU4S/dOndi3W5ZFySkitygEGiU7aGvI7O+2DxdB0ViQv3MKyMpBJOSSdLYESTzRy+JJpLlnK60Z3S+Z7vlbnVEB2ntpglW9zu06p77f7687jy5KXgXp/BAEEu+gvZGOlr6BLV3vq3upDm2Z94kkvbmqMx3GsTOHnDKycsmPBt91eyuRZvQKT/7fgLS0B9xtnS1aB/D57Oyca/3BZhsd7yQlhqiyORMFuS+SQnaNOrW+7JPe5l3mmCcl5Q43ud3NRhrtIIeGsPvcYnIKJeo7VdumRrW1Vnva433qC4EbPVhkgd/6uVW93AcDoVGjP/q9R9yvXbtznG+yqR5yj7vd4UmP9hmPDu1+53Ived7JzjDSKC9b5xf+0yPu7xPCn5bWqMEmG22y0RKL+rTdprUPUd/jDjDLHEOUSUu5xx0e8YBnPdGHyJKS/uZaiy10urcaZ6ItNvmTK93vLrVqFCqQJaZOjav8xiKv7mBNLfSq37q8m9zT0m7wZ03qHeF4xYottcjlLrPScuuttUbPZJsnzxbrXeXXfSJId4YN1vmMDzvFmQ53tDx5bvE397jDAvP3euGya0Q45EJO+EoP8d3/vT7EVzgkBNuVDmfcrDA9NGwP7r/CMuI5NNcxZkYgyUgkKKYdrcHiS6dCHcWVVI4JhLj0WT0xtK9W95AeYVX/zJZAUJNKdiS+NY28MMjI4CHZjC6iIjdYE1MyT//IGSBIKBZl3nia2zhkEsu30Lrncu6g99woGcWxnyGnMMjNz/2R9S+Gz+L5TDyKA85h9EEUVoTkxkQ7DVtY9xwLbmPd831JaU+QU8Sw6Yx7E+MOpXwS+WVh14FIJKxk2huo38j6l1j2YFgUdeyFn37WWczIPDGmfiOPXUF716YdEcrHM/NMpp5I+YTQt0g0SOtN29m6lNVPsOwhatfurJW+iOdRMZlxh4dX5RQKK8nOD3UnO0MfGrew6dVQ99pnaN2Hu6L3ICIqV0yBqFwRWYJA1SGpWUqL9F78wKNyxJQgKaVdWkJchYQ6SS3yTERaq5UismUbKqFBsk8Ie0RMkaRGMUGjyZInIqJTPeKyFOmwTfp1RlfmynWdfxhupLOdaLutYWeNqSew7OHuzN2YmDQDpx1MOY4x83jo8tfVlz1BTExKareSW1do/2DK9kehArlybO+XZ9m7XgyaCMIYprtJOibm7c5wuhNd6nLL98DfN8l4b3O6n7uyu86dXd/xjvQ+b/dXt3ooIwkPFlFREZE3iOx6IbuQD93O5F6BOfd/l3sv6f7nyKkcfCqblrN9A6OmsHI+ZSMDGa59jYlzKalk43KKy1i7kFFTaapl61omH8z6xYycEubUl+6l9aBR3PlWfvA8P3yhb78umM5VJ/Gxh7g6sygqyeb+c+lIctrtNO7iN5gd5fwpXDSbkQW0JgKh5sRCQM3PXuKrT/U9JzfOhzLjsKWO9dU8v3KPh3TQFl9+OYd/IEzG6RS16wLxlU/g5G8x51xyS3ZMZKycwsSjOexCXvobD1wWyGSwKBnF7LM48B2Mmhv2iItEd2ynC6MPCqR07KdZej/3XxqIYk8w7jCOvji8376SF/4cSCcrh3nv5MQvM3RaT95obwydxoQjOfR93Pl1Hv7prtvKL2fGKaHesYeG8Y3Edn59DmTayRx1Mauf4oEfsvKx17+NT0RcrvFKHMOlL1oAACAASURBVKbYmxSYJm6omMJexNemU402azV4Qb2nNVsoOcjouyKHKPVmERFt1qn3rBJHyFJkoysVmCUiqtVKFc6QbZhGL2nwXHcdUXmGOV+VGwxzPiJyjJSWUO9paQmFDtRuo22D2GC67xhE+kyQI4w2yhhbVYXtsOK5zD6Doz5E+TjWvci6+ZLj5jH5GJqreeX2IHNMPT4QXnZeIMhoFtPfzKg5bF3OwnuCDDL7NIZOoX4z828J0Xj9kTeEA98aVpTLH2Pti3a1Xt3ZRDxMnmYJTZkUgnRmA4BdIUsssz1cnnKlVlgjLe10b1auzD89oMr2fvuG9tSbJ9eEzKOMVlunRYsRhqlUYbsaGzOBNMUKZcky0nDrbVSjzq3udpTD5PZKryhSaIIxatXbYLO0tEIFJhgrKWm1dfLkGme0KSZISFhlXSatI89EY7Vos9Z6SSmPeto8BxiiuLuNXDkmGicmZq0NGjTKkW2icd1tJKUMUaxAgSGKrbZOk2aFCowzWlraauu0alMgX0zMSMMkJK2yVlTEeGNky7bKWm272vSwdCyV03b5PbU18sqDrF8Ubqv1C0mlAqER5oeta5hyKEueDmXSadYtDJ+nUlStDrukrH0N6X4q3UDWV3YsFOwcYPJJd/9v5zhtPJcfx/NbAnkur6clEaI8bztj4HPaO3l1XehcdSONexdEtndSZ4ThM8Mk/47fMPn48PykLnQNWNfkHYlQUBEm6/KJ3PQJatYMrp03/QcnfyNIpwORQf+2ut7nlzL3PIZO5a8fZOPLe3GdMtJtSSC9Yz/DW74e1KudElOm/c62YHHuDjNP4/zfBat5T64vp5Bpbwnfwd8/ypJ79+y6uuuSo8SbjHChUm+WY6TIAIELfXGUYd4loU6jl2z2Z9vdKTHA6r83ouJarRDJZGkVmC6tQ45RGcJbpUDYk7DFUtmGyVKstzARgi2KMyEXpZIatFiGtGzDROVIaZNr3B6NQ0zMB31MqTLbbZOvwKnOMtxIv3NFCPFPxQO5tTezaSH1WxgykiM/yNN/ZPSBHPZeFt7Noe/isd9z0NuDnjT2IGaczDPXMvecIH1sW8kBZ/LIr0InUjvZyeOw94SZa/ljvOnCIKPUbhigLENkyxWzTZscMcXitmsTFTFCvrWZAJMicUWZz6BCrkadOzxzcIRhvumz1togKeXvbjdEsbOdKkuWXDludKcNNu3Ql0L5vuJT4rI0avIP91prg7c7Q1zcAWb4T7+x2jr/zxfUqtMp4UGPey6TuJ/uJdOWKvFln9CgSaVy/3CPZ7zoiy7WoTMjN98HZprqbKcYY5R7PeIBj/mCj4FiRZ70nNvc08fKDKMd8R/eY5hKzVo85QVPe8HHvF+FMjExy6xyh/v8xCU22CQqqlPCpa7wJgc52ByVym20xRX+4GTHOd6RttiqWq2NNjvHqeY5QLMWLVr9wn9p35lCMfLAsOjZBWo2U7NFN9ekMpfUe0Hc2c7iJ/sSWvctV5An1dFJMiE9aRzba6ntpbSMLQqBLl2+oggml4TcsP4pDYNBFKeNC3X+4AUe73X/xCLEd5ILmcYTmeS/ssIgfe4F9or4IpFgWZ3zM6YcnyHf1UHKrFoc5LecQkbMZsLRlIwM50RjTD+FMy7lxovDwniXSLPsAY75VPje0+kgXdauC3Li9pU0VoUvr7CSUQcGq6mgIrQXiYR75pT/x3UXBv/bniI7P9Q97vAe0kslqN8U2m/YHCTOnGJKx1A2PvS1ejVbFu22equfDnVVTgnXl2ijbgPblodXw+YwR+aVMmJW6EfJyB6rt3Qsp32XTQto2HHu2SXyTDLWFwxzvixle5QWEBEVV6bUiUocrc5j1viBOk/Y2W56SW2SGjMWZCCqpEYJ9aLyFJol13jZRmTkzKYMgfUQX0qHlDYVzhKVo12jpCakReWJG63T1kFbob1RrsJZ3i5fgZSUjdb7pi+42V+DJZjsoGpZ0LPXzw/W2eSjw7/XvEBTNSd9nm0rqNsULMKiSkbPZdQB4ZyNr4ZjE97EyqfCl33ou1lw58DEF4szfAYP/5Kty5h1GpWTByS+yYqdaax1mjxnmzOM1SahVodHbXas4e6zUYuED5lmuzaP2Swu6lgjlMvxa4u09fr+YmKGKPENl/WRNR/wmDT+6Iadjuds0w1R7Eu+28sijLjebSqVG26oqSZZa4NhKvyX6yzMJLkPhIMdqEOnH/mVuWa5wDu87DUVyjzleQ94zDY1Zplmg81+4SpTTXKR99qsSqVyX/Qdo4zwVZ/0gMc09du2MCKiUrlttrvV3TarMtpIB5jhcy6RI9tlvuEZLyqQ7yrXa9biMt9QpMCTnrfaejNMca7TxGUrVmSbGj/ya2lpxQqd5FiX+Intqv3EJSYZb5FlO150JMqEY3p2Z9kZJo9l5kRq6qlrpL0jWCNTx7FuC6+EcU3n53H0vOAjq6oOn7+4iDOPZdM2nn+NQ2fz6tK+xHfkCKaVsihzD4wt4uSxrKoPeXd7g67VfLIXExfEQzRo/0hRKM5j+JAQ1FKcz8hSnl7Gtt0RyY7Y6+CWEbPDK9HOc3/i0SuoXtmzYSnBzzdiNqd9J/jMorHwmnMuyx/imavt1hpe/xJL7mH0PBbfw+K7A6E0V/fszdqFeF4ghjMuZfwRPeQ39c2BEJfvxcMSs3KYdQZz3h6CdrYu5cnfhn7Ub8xsxpoK8mR2QSCiiceE62weRBpizWrm/52D3hX8kgvvZNMrNG7tm45DGM/hszj1EmadGdqIRMLYzDiFZ68Z7FVFlTrBZD9SZJ7I68hqCRZYrjJvUWiW1b5rkz9JDyDdNHmZjF9ERlbMUiSpVUqrOo+LeEpSg2aLtNuoU7XeGYlpnbb4k5giKa3Skhl5Mp25shwRWVJ7uA9rUtLP/dBVfiNXnpSU+gEfQJvuubGgvSXceNFYWO0l2kN0Uzw3HMsuCJNXWyMF5YiE1VN7Uzh2z48YOYvjP0ntxrBi6tNcMkRt5RZm6ssP9Q+AqUq8rNojNpuoSFLKbdb6sGnutt5mLeKiElIWqzNJkVwx4xVJSBkpX65YH+KDGnWa+iW1hxHf9Y+3WLEadX1kvJmm+IgLvGKhIUrEMvdekxY1du2wLlGkToOUlDoNCuTr0Oknfutcp7vU1/zGH3VKaNSkQ2e3TDlEiWYtOnRq1JRJKsmm332SkvIbf3S2U13ii25yh3U26tCpLbMTDiEopkGjBo0ZqzEtV66LvC+zC06ruHhmyZa2yeZuy7IrNaRJc3e9Rfo9B6wLeaWMOWTXEhMcOJWtNUybEKy1tnbGjqSkkFiMV5eFlfXE0Rw0kw1VNLVQUUpWjM3bWbSSyjIK85gzlde6ooHTwQr77QncsTqkIZw9kclD+MZTbNkLiy+Fhzfw7qn8v8O4bmlY3548jqlDqB1AwmzrpL6F7DivrKG2iZa98+PvNfFFosGcfuE67vjKwNZbsiPIfTd9gvf9ORBCJBIiFY+4iNfuoGk3gT+JtuArIxDNrnxZna2seIRbPssHb6Qso3blFDPpuL0jvmgWR34szGOrnuTmT2V8hv1+8+lEeDzH5gXhFYkOzu+WToXgmeeuoWZtsCZ3hmQHG+dz62cZMjpY3ZFI6OPUk3j+z7s+P3NFKpxlqsvlGrdLK6+HUNLd5+6sfNiHdaTJfiym0Hq/2iG4JDVAUndHrwm1vd9z0nZmtQ20N2tPG3u/lUyHDttt23Whtoaw2jn6I6x4jC1Lw7ETP0vRUF67m02vMfdc3vyZcBNuXc6Kxznxc7z508E/+PiVlI0NsmcqQWv9wJFYqSQL/skh7wpBNckONg8sJazQ4HRjFMs233YxEW811iqNyuWYqDjz9JTmzOObgvw5Ur6ktHqdOyWz/kdbtRlntApl6jXoHCD1YK313uNcs0xVr1GjJrNMU6vOvR52mHk7HeaYmKEqFCowTIU1Ciy32nGOMM5oh5lrjfXdub9/dYv3ebuDHegZL5pgrCkmmG26zbZaYbW3O8MUE0wwznY1mrUqV2qIYs1alCrRoFGeXLe7V6MmRznMC/4gKmKmafLkaNehZoD9fosUOMB0l7rCJOMd4eABx69Boxq15pltsypFimzot5F3N8onUjZxp+PUjVUbmTsthIvHYoHQXl7M+FEsX9ujb26oYtkaNm6luYXOzkAkG7cGq7G2IRDipl6/gzT+uoxEivdMCzu4bGvh609xzaJ+20OlWVYbJNDUbqya21eFoJb3TuPbhwf/3jObuehBPnngjhJqR4Kqeu54IfRlzba+PrY9QAzfHkzBomEc9v6ep+1C3Xpu+XT4uyu01QfrZ9ZZwYKKREJE5rrn2bpk1+cS5pW2BoMO4W+qomJK34VS83ZevXVwZDTlzUw6NrzvIpaaNfztokA8g8IeBMl1tNBSO/gAlS4pedpJPZJnZyvzb9h91GypN5vuNwOSXjqzsVWLZWo8YKsbVfmbrW5W7W4NntNhi4gsWYrot2tK2IY8R7HDddikyQArhH93JDuDhNlaH+TG1jrWvRS+wJVPsv7l8GWsfSEcW3h3KN+0PZRrrWfRvVSvCY7glrrgs1twJ007Id3a9UHmrN/Ma//sFWLcF9XaLVdvm1ZbtFqkzmYt5quWlLZao7Wa1GlXq8Ny9Zaos1SdzVo9YYtGnX2+sZiYuLjXLOnjC6tT7wiHmGe25dZoHOC5lnXqdeh0tlMdbI51NlputYPNMcdMK6z2miWqbFMo30JLuv1cpUq83Rly5KhQpkPCfAvEZTnbKXLlutaNOnV6t3Oc5Bgpabf4p5bM4mee2UYb6Y9u6A40Oddphqt0rb9r0OgsJxtuaCZ8J9cq65zpLc5wklJD3OQOa2ywVbVznWqyCa5zsw02y5dngcWSkgrkedECUVEnOlpExAKLvWaJHDlq1Fmf8YMmJa2xwRlONM8B7nCfBTvbnH322Rzwtp4ty7qw6lFWPtLz7y3b2VgV8qMeeT5Yaxu2BpLbVtPzM2xrD8c2baW6nsWrwt+q7azbHAhv8SpWZxah44sDMT20nste5OYVXL+Uqxfz+MYdA1s6UzywPuTvNe1m393OFM9WhTr/tpw/LuKmFYHwHt4Q0iESverPioUdWwpzKcpj+ijyc0JC+x5i0OkMI+bwiQeCv4uwgHjp+hA40l9yHAh5Q/jw7SHCs2sj0id+E4hzXzxYsD8OfT/vvqon8nL5Q1x5Vt9dgHaG07/Hyd/s+Xcqyb3f5b7vvzF93RtMOo6P3BGCb6BqCb8+IcyhO0O+qWa7XqF5A5BeQoPnbfBbtR7SoUp6gFV8RJZsw5Q52RifVOhAkX6b8qaltVnrNedr6Pfcs32C/AJGjWH0OCoqQyZuawtVm1m3Jvzt7HdTRiKUlpOdnelkmtoaOnaxUohGKasIDxLrQjJJzfa9ez7Kvq6vNwoKGDaKUaMpH0p+fpBkGhvCeGxcz/aqsMLfR4iJiYoMaO31RtjlRXe5rm3mBpOg3nWX9p6k4rIkM4+OomtHnJhE5mjv/tE3yjVLlrRUn8dN9W8jItLdRt9zQ9rKriJhI5llYUJit1LwbschFuddf2Luu3aUOvulM3QjGtm9pbUnOG4Ud57FpS9w2Qu7L/9GYsJQ3nlE2KaM4O97YAFP7twvvDPstdSZTrHm6cGRHmGhu/qJQHxkfFNzw8TdtueEvVs0V4cHHXYRXyy+46JpT+paeMf/HNIjGBqd7T3EF8sKfsadISrfOF9VaO4OpJfSZpOrrfGDHeTG/khLaLfRZteo87jJfqzSW/uQX0RErnHG+apFPrBXgSYDoqycU97KW89j5hyGlAWZpkt3b29j6xaefZIbr+W5J2nPEFvxEK64msmZsPCOdr78cZ7dRe5WeSW/vpYx43uObd/Kx9/H+kEmaPZGxVB+82dGje05tm0LF1/AxnU7P29niEQZO55TzuItZzBlemZMsoM/MJ0OhNrWytbNzH+BO2/iyUdoeP0/uuQgM9j6T+x7kvc20BTen2hTmZCnPohlS8b6BYSkkhL9Hec7tBGRzhuio3QsZRMoGR3kqVi2RLIj/PAatwSZq35DsNJ71ZmWHvCJEwNh5+MQCb6V8kmMmjewfy+W3Vd+21Mk2gfjF/mfhW31XPkgNU1htVJZQta/MKqTkDC+bfcbHfQgHQLaUolAQoQcvfyyfUx8kV6Ptkj3O76XVdauHWT6xb8CXdcRMqb7HN/VBZZ5i6HetkMgS1rCJtdY4Wv9EsV3j1YrLPc5ceWGOGYH2bPMycqcZNsgnja+S0QiHHw4X/4ORxxLds6Ok0E0SlYhEyYzfhKnnMkN1/KrHwcyrBzKgQczPPM09dbWYDnuCrGsQHqTpvYcyy8IFubeICtT34TJPcfy8onvxX6DRcWcdwEf+jgTpwa/zoATZCwQYXEJk6Zx+jmB+H5xGS883RP33g8R5KMgEm6zprSdbrudj8JIiOVt6wqnt+MGQ7koypRrSNvBzolkyrRnzisUdkRqTcvE7fYgmmkzT3jg8g71Hf5h5r6zbwNVi7njCwP7UqNZDJ0RZMVpJ4fdJHJLMpNV73FNBwmoozmQ3tYlrH4sBBdsfnXwlkDvdrMLKKgMjvvKqQybFfKUyiYEH99AmPduxh+5Z211IZXiwUtZ/sDuy6YFv92+fi5QHwxSeGxqRzsHTWDGKJ5eTt3ePUlk74mvNVhCe4KGTWGh0UV8OUUh365m8JsyyMoNcmvJqBDWX1gZZNTs/PBZPDf8LZ8YFkX7AvWbQurWvwKxeEjHKBkZrrFwaBij7Hyy8sL1xXMpGh6CdgZVp2KjfVRM3xPS0ho8b40f7DHpdaHNWmtdptAB4kr7tVtghAtVu3fvg04iEU44hR/8IhDG7qLbus4pLefDnwpE841PByur5F/7qKA3DCNG8fVLOft8cnIHNyZkEmoLg3U48wB++E1u/RuJvhRUHuEjOZwQpyISpqQ1KX7XxoOJnvVWFs6I8x85lGUWZJ0CsW1M8dUWNqVDubfE+XAO46OBoJ5O8Ks2VvTi3VFRLsvjl23MjHFBTujLtjRfaeHFjIE0PcpHczkkFsivAy8n+HkbS7rqK5/IxGP7Xn9BZZh0+hNf0TCOuJhDP0DJmN0ETERC7ljekPCqmMyMM6hayNVvpXbNYL6IkOc14WiGzaRiEsWjem1FtYsdOrpQOi689gapBM9WDq7skho++lB40sIbgfEHU1TOgvsGVz4ni8Mnk5vN1BEhib1mYJ/3rrDXxJfs3GlU9U7R1hCIL6cw03hOj1S3K0SiIU1g+ilMP5URMwMhxPPDorz3gmywc8CeoL1pJ7nF+xBFw8MWaDNPD/mIRcPDOEWz+kq0e3N9JQ5X4qgBJc71fqXdwMnQg0Wdx9R7UrkzdrD6ShyhwDSNBhsV1A9zD+HSK3YkvXQ6+PU2rWdbxneVX0DlcIYND4SQlcWpb6W5iRVLwrF/dwwbwQ9+Ga4r1kvb7hqPrVvYtjW8z8oK0ueIkZSU9kzokUhYCHznZ5mnKV/fx/LLizAjxhOdLE5RnCHCyws4q5FVmaIHx/h5Prd1cmMHoyJcms+WFFe2U51ZxJ8e54p8Hkvwn23B6rswm98X8IFm1mfqy8fRWWGzm2zhIdAtmBCltpdBUBllRISbO8K5k2J8IofyKO9v6p+g0Au5xWHCaezlCB8+mzN/zJSTelbke4Kue7JhE827iQjuQiyLYz/H7HP61vHfgXhu2PS4taEnFy2eG9JnOlrY2sINy8OXkl/aMxl29TmVzPiTMpZwLE5eJl2nM+NmiMUz6T4FweeVTGQm/wIqx1M6MhBfV7vtLTsnl0SKuhaOHhOS3K9/cq8u+3X5+NJ7SAbJzr4EEomStRurLKcoBKoc/fGQ5B3N2nH+C29Cn5Kp8DcSCRbfvrinUklvWHBiVk7Y4/T4zwfC69/n/teXSvXMUVkDKH47IqbSOd37WvZGs4VqDELu2A2Smm33T+VO1f+WiitX4ui9I77Scr7yXSZO6XuhHe08+gDX/p5XXwr+qmQySJClZUHSfNcHOPbEQHZnn0993X/vBLMvkF/AFy8JPr0u0kunaWrkvju45XoWv0Z9bVgIRKPhnLHjg2/0/PczcnRPHmJ5BV//PmtX8sIz3c1sTPGR5h7JEbaluKaAabEe4jsuHj7/ZRurU2H9eXBHILqlyXB+SYTP5LIoyWdaqMtU+FKCmwt5TzY/butpJysSCPTdzazLtNNfCHsqwf9n77zD4yivtv+b2V6kVW+Wbbn3XnChGbDpBEiogYQECCQhEEJoeUkIJQRCXiBAQg+B0CGhd1zA2Ma9F9mWLcuyetdq++58f5yRdyWtpF1ZNub7vtvXXvLOzM7OzM4893POuc85q0IczA5Ug/I9l5ghRxXrNC7MDhm82zFgCnz/SV3+fSgd2jRRWCZTGFjtxjV9JJE+AOZdLQOJpxG+eEKIZ/51gAat9bDoKXCkwWm/FiJTjbD+PbC5hMC2fgGTThfi3LcBTr4G7Gmy7ot/iKts/i9k0HJmwrKXoL4MTrtRiC49H0rXiUtr3lWQmgsWO3z0EDTESRcIR+Dd1bChVNycqiKNaQ8kl0Tf9+4MSvL3ihInztaT69jqgjPvlbJlRms0dhfyS2y5ZqdUT2k+IL9boE2U5EGvJKwvuKNvk7gjBaMFTrwRTr41WudU02SC0FIZrd7SVC5u5YA7en7pg+HsBySpvieYye4SfwNxczayiGBveWsJopV1hGjt4u4EFRezOcATcVWiPeLsH8DceV1J7/kn4KF7obGT+8XnhdZmKNsLXy2En90Av/ytDP45uX06r6MGiiLX4wc/jKpCNU0Um3+6Hd57S6y8zvC0iSBn41pY8hn8+TEYMyFKfoWD4frb4ZeXiwIUIZkQkKOIdWVH/mrI/9thQtya7b+qhrg6TQoHpU4DVbEeH/RFSQ+ECLeH4UQjPErHDiULQ1HSa99vLMLIwDVQFRerVT8WkyKWYrcwWsGul/7KGgHnPQYDZ3QloHaBQCSsC0A0UY61u186b+9rEeVeMtC0xNxI3bk9tUjf424R/bMzzhdiWvsuLLgOiqbK+5RM+PghaK6WcnuzLoS6ffDVC3DRfZCaA44MEXqAEKjJCmNOgJQsWP4KzLoYJp4K69+HITPgnXugaieEgjDmeGkX8e69cMov5NoGfbDqLRnQ5l0NgyfFJz6QpPXtughvSpFM8I4U8amG5GNoBkvHyjtauPu8M0WVJPdZV8k1BblPytbAsn9IYebWKt2ajvP7m6yHOR7bDxh/jpCeTQ89aZrEyr9+Aoo/E8ILeuOrSQdOT0yUZWdk3LqVGn6aWE5/mbJ+KglS34X4FBTsjMBACqE4Sb/dIjMLLrkimn4AcoG++BgeukdSEXpCcyP8/UHIyYPLr+5zoutRg7wBcPX1HQU5bW6J0735cu+DaDgMK76Cu28VpWqmHuNRFLGMjz0JPn4HEIvrRiscaxJdgxcRudg6jb9LQ+IC/akZ/huEPAXOMsFXQWjQb6tUnZhqOt3DAcQVOsIAFl3A0o7aXtTT0wxyfCMNIqbxAwVqAuI1g0niebYMOP1PMHh2lFTa6wXWl0jVjYqNMrv2NcsDaLJJ/CFnNBRMlL/OXCHDut2JJSS3IxKGrx+FLb0UUU8tgHm3iXXVGVvf7/3z3UHTpGXO+F/J7Hv8KWKFtTVA1S7YulCsvspiWPIcuHKhfKu4L2tLo/tpv+Dtz1bWIMgohPHzxVVZrxNXW4PsP6BPb1JyoFEXTtTsgYwBkDlQSLBun5DnYbZY+kx8icbnYmHTiz23I+jrvl5n+mCYHUN6mia5eG9c07WqU1wcgorzSMCWBsf+Uiw9kPOr2ASv/hTK19MrHym9qDjb4WQiBpxdlodoxhvTl+xQEcZNqJuSU2ayMZKWHPFNnQVjJnac7TbWw1MP90567fC0wbOPwcmniWXzXcapZ8GY8R0H6s8/gHdeTy4A/fVi+OwDcQW378tmh/MuhoUfYQoE+J1N3JW3e8Wt6NZgsgFe73QbfROC5/zwc6uIV0LA4iA85ONgzR6frrZ0dLpXDYBd0dd3roLUw+FnKxJXVIGbPbBT72d5pQV+3VsIV1EhbRDMvgbGnaOXV9JEibl3Gax8BvZ8JeWkup1VKuKWSxskqspx50ipJk8SFocWgd0JlJHKGQPH3QDEIb7KjbDmxcS/Mx4qdgjBLH9FYk5tDfJ+22LYuxbOuxPWfwANByB/FOxaAbnDoaZEiC2jUCy/gjHQWgvVJeDKg6/+FS3TZ9YtkFgrpKkChkwT12f+KNlX4XixIFe8Kst6gkEVkQvoCe7JCzD6TnxWSM1L7jPpgzoSn7e5+/tl0HRR87bD1ywteBIiPfQeeT3ktX3byBklE8eDdVoD8OUjiXV0gBhhT49QcTCOeAwZolkvGJ1Y49feoKDGLUkGYMCJMd7D2x1UFU6cDzZbdJmmifx+Q5JJtCXFMthf9OPDHlPJToP6lm4zBPoOZwqceT4YY2bBnjZ47YX47s2eEPDDx+8K0Vn166soMHUm5BfiLNvDMUZYGRLxSHtGWpYqllmHw1JglhEe9cGLfklfaO6UVlAekdcMAzxPdH85KoxQ4ctQRzdnbyhUYbQB7vXKZ0FIdEAiFp+iwKQLRDZtMMs95WmArx6Cb55OrLgumsRUarbLa/2r8iAeTUm+iWLVW3DilXDO7UJ6i54WN+bcy+QZ3P2NuDs3fQrzfwmn3SCuSE2D4mXiHj39RqkxW1cG+zdD1mA4+1YRqHz1vBDi3rUdizjvXQtDZ8AZN0nloortYlGOOlYszfqyqLXYGUYVLpkLDisEQ7Boa9JuTjgE4jOYpAD1xrcS215R9dqSMR6nprLuiS97ZEdrt36v5AEm9mUihDmkePVhRkZRVN0KkhK0d3m3m3dBZpFMPnqCihkrg+LW1zSTxxieIZJgsm1vUDDgWJUhggAAIABJREFUYEw360wYsMVdFxfOFFFzdlD5RGDJ58kP9KEQLF2kx8b6133isEF+BrR4ZCyYOQYWr4fMVInBVyQyjiaComGSsB97PUpLYHMflbI7t0FDvQhd2pGTB8NG4t+3h9oIDDUIwVRHRNByrQU6zyOdChQZoFKDubpb1A+UhGFnRObhdZqQ4o1W+JEFFgbF9flzq1iBrwaSm6+3aGKBTjHCwKBYiyeaxEJNyGmfN07+tpPeh7fA2n93GJiLMmBMnoSS1pRBWwDyU2HiAKhrg03lkGaHAWlgVOV+dPuhvAlyU6DAJb//2v3gD0FhmozXY/JgTz0UV0O6HaYOlLrL6/aDt/8K6iQOdz189L/iVgsF5Bq4GyQep6jiktMi8vfdeyVOddLPAEXK9b31BxFZBf3RuOiip8QtHImJYy15tuPEwN8GnzwiVlDQF/3sf+4UayXk6+HHVCRx3euX2p2+oPxNEn0vUq1IFRZLSmLtfhxZMGRuR09N2eruVasWZ8fn3NsUv0dnPNhc0dJoRwpGIMsoRczrQvJA2lVoi9C5pgQQ7azejkBb4m2TDGYYcbLEWXuCihUT8ft4GUklg1MS+8JDhopCEqSTldOxuglAWxts7mNTxZ3bRfmYltG3z3eDMYNhWIEUjwkEhQBnjoZhhbC9FCrr+ynOPHaiqFXboWlyTk19bAfT1Chl0mKJz2KFoSPwLPqEx/zwgA3edArRBDX4T1AEJLHp2Q7Emltggrn6SGJBiOweL7wYkHv/Ob+oLn9rhd9YhUBbNbjNK+7SdoQRi7GncrNlEfi7X9IXPnSKldmg5xhebIn/rMVFOCCWXifSy7DDLfPhm71i9OyqBZsZbj4FNlfAKaNhgAvyUmFwBozMgQ3lQnhvrJPPvr0RxhfAwHR4awP8eh74QrCrBlp8sN8EN86DyhbIccKEAnhqWT/dK8kiEu6UpKwJGXVGOCSv6t3QoncWCMeZtWha10E9njUcCXfdLhToul1nKPoxegJCen10r/Rd1YlYcEWzRYjRG0YvkLZi7fC3QvEXdMvs/la5hu3kZUnRJwi9TfgVaYE0cFov2/Ujsg1waw6ckiJpUet98EUrnO+CGw5ARZwJib9Nvx908jLZxH2ZCAYfI01oeyN2FQuG7tqdHEEkPf/IyZNKI7FoboSqnsupdYu6ahns+5n4QmHYUQYjB8pvoSoy+dxTIctW7xBvzKFBkdheZ792VQXYnfRJnGS1Rku5HfwaRRLjgU+Corocris590QkX+69gFhwIHl0/3DApjD8qo2DvTJSgLvskmD+TlCIrBX4kw9eC8AgVchzV1gsxdijL4/ARW6xMrtDEHGtfhoU96ZHk9SJFg0+DkZzAnuEpokK85unO7rgkOdX08Bqgk+2QWUznDxKiG7xTqhzwymjhBC/LpHPfL4DLp4GDgscaIKXVgnx/WQ2vLMJbCYhw2V6SH1sHkwaAOvLxTKcNxL+vUosy6MeGz/6duvOhyKwdAccMwK8Ovn1AYdEfLY0mH871O3qOfaWNw7m/TbqmtM02LdSLL7uUFcCkWBUOZo5RLq+7+0hX1FRxKo89Q+HVsYuGRiB32TDD9LgzzWw1QdzHXBnrsirLd24W5vKxMprV3Q6c2DwDBGV9YTcMXDWfSJO6w0KRtR+iuEdUWRld002b26SVIW+oM3dL7UpO2OnXl6zrFoGsPQUqG6AnHTYurc/SI9ombPOs5xzL4Q5J/Rtn6rasWxaO1Kksk8EyYXrnA8XW2VlmO4KvdsLxTHLFWCPTpqxg0sI2a64B2LyA1sS8HsG9e06b9vTvjsg5Os2ptfkhXs+gdPGwF1nwkOLhLg0wGyEklr4ajdMKYRgWCY/wbCsV4BAWK5fICzuTUWR/7fEGFFmPRfZpEJ9Gzy7vE/eusOGQQwhnUw0NMrYS1NMA+LOZqmCQhHDScVFhAil7KY1pgpUIYPIJIdqKqiigjTSOYbjyKOAMkpZywpakGfTQQrTmU0RQ2mgnnWs5AD76cC0BgXGFcL+OshPh+wU6dGXJA4pgR2kx94lz8MX98O+b0TIo4UBRdyVQ+bAaX+EggnRZ9fXIp0ZeqrRWb5eSoVlFMl7ewac+nt4+0ZpBhtrPSuqEMeEc+Ckm6W2azgYbXx7OJFvgvNc8FoTPFsvlv9qD0y0wpweyLd2l+QhDp4p740WOOkW6clXtrqTqEyRvNuRJ0v6Q+GU6PqeVb8qR7e2tRukpnVNP3C3drVSEkUgIGKQfobH3/Fvi/4VZdX9+CUmkxTnjoWiQH6hvPoTBmM0mbQX1EWgTYMfmCUtoUWTmN0soyx7V7f2jkrUFEtbnzhIscCgdFi1D8blS7xuaxW0+aG6FfxBqHXD5G4u/dg8mF0E0wdLLC8Uh8jLGsQybPFDTasYLZ27+3xbUFG5jlv5HhcRJszNXMOH/Kfb7Y2Y+B1/4njm48fHz7mUZSwGhBSv5tdczE94jed5gr9yN4+wgLOwYMWLh094lz9wIxHC/J6/cA4XYsdBiCA72ModXM9KYnIkVRUsJiiulBmEvW8T+z4TX+1uSXMZPk961xVOhqrtULtT4nEmmwhUBkyKJmeDENLKf0o39Z5Qvwc2vCkVTdqLHIyaD1e9K53Kq7ZKzM/qEoXk4JmQO1pyBf1uWPo4TDxXLKTDiQEmyDTCKk/U3e3XYIMXZvdAfG0N0rm+YKLElhUFBkyGK96QtI3yDeBvEcs1a5gk5OdPlNhgOADfPCeW9PAeJ/0Ruot6+CijgYVofZACJwsNP4HuGm3Gg80mfsNY+H19b90TifTcfuhohtEE1gR94EcQuyJwpxeus8IrurBFRdyYbwTgb76uRaiPCmga7FvRbe9DVRERS6YD1pXDshJxQT69DOaNEFfoB1tleZM3Kmp5d5Osq2iWz/uC8OZ68QS8vVFcpu1o8MDDi+H0sTCxQCzIowcKNuy4SCNECFMvsXkFsOPARRo+vBg7bW/Dhos0xjOZa7mJUzjjoEVow87ZXMAOtmDCzPn8EA9uggRx4WI8k/kNv+dqLjxoFRIIwVfbYc5IqGyE4oo+nWWfiS/ohQ//R6y5UacIARXNklf7hLGzdybkl47tn9/XoZNHXERC8OWjkD9BCE81iGWXPULqwgJR/wLRiaq3UazPZU9Jg+uc0YdX5GLV8wU9nfjFHaFnX7gGa1+GQdNg+uV62yRFCrRPv1xe8c4v6IUVT8Mnd8MJv5ZJR3fnFyFIpBupQBs7KOY6IkmJyQ8FSUz/lTiWakRLbh8dvlo7DDkGRwiqInlLsWhvN9Tf55TExCIMvBKQWFuBKkpNL5KoXqf1JbPqCEELw/413aYfNPvE9dgZa8rk1Rllempq7W6YNkiswie/7tgSLx6x7ayR1/8rmMR0CijkLm5mJUuZzfHcxp9IxcWP+TkqCv/hJV7kSSzY+D0PMI1ZTGIGIxnLGlbIjowGyEqBF76UWUUf0WfiszjEFfnaVTDvJph6iSTkd66wo2lyjzXskwF7+VNiESaC5nJ441pY8D8w8Xxxd7ZXWgIOjo3tRRcObIJFD8KW98QqKv0Gplx8ePP5WnXZdnqn70gz0KuX0dcM790izWNnXiF5kR2uX8z5hYPiHl36OKz+t5QvK1sp523qJlMggq/bXnhGUlDQi8sebQgFOyqbQG74vuanKHz7dRFBjj/ZCjKRSFd/mRaB5x6D5fHddX3G/n1JSQs1hOTqjlqWi4OgDxr6r3BDLMob4cMtR3/FqG8DDpy8xRe8zLOECFJKCcczn9M5lwIK2cYmHuSPVOoF81/leaYwgxRSGM6oKPEpwIh8yd1z+6Rg9ZFMZzBawZTvpHaDj/dvC7H2ZemcMHimVPYxZlgJujJoWlbNniVhdiw2UltuQWtqg1Q7ZLlgn64KsJrBZITWTkFKp43G2gj/vd7P6hdl/4WTJZ5nMAkZeBqhrtRAyUYHJW+30FKpfzbVzrZFQYy3BlEN0HSg+/JonbHjM/BHLDLTbvFSubX7m3l/EMoDouh8p1lKL7lUie8lMtS21cMnf5R8yNGnwaCZQoAmqyh+vc3Stmnvcti1WHoDtk9Wy1bDe7dK4XRPU9eYaQQfAeIHnIxkYMDZ53ZEhxVtbv0kY0jCZu/YuTwZqAbp4fdtw2RKvvdeMAiezm1XFNhVLIno3wUoHD3zq6A3uSorSaC6VV7/H10RIsgKviSk5w378LKB1ZzG9wBYzTKqibotSyjGhw87DnIZEN1RRJOA6/emS0rDh+ug/AgmsKsGUE6fDk27Ce+tYv/aEPs3GTDaDZjUAEqGkciVxxF8ZiHhvXWSsJKdAg1tQnQXz4Mn34eGVsn4tZi6Et/0kRAMEVxdzJ6vQ+xZacBoUyQmFgmhGU2EvBohqwPtrDnQtAhUXRM8IIv66iYWPxwSa0FDZ69wVFblD4r0ymCQRCyjAYJh9qwwsMdXBJkpsHijbKcqonH2BwHloGSr1h/k2Qa4Ixf+NgC2+GCKTWJ/kQQf9nAQatYbcKx3sdbsxmsLoOrFIMJ+PT80zqzaXQtLH+t+vxpBPJSgoXVJYjeRiYVcAiTnI0/PgmlzpMLR+hUivhw7RY5x5RLRoBwyGhok8TxWwp/iEvJr7QNRmy2SFP9tw+EESxKJ/ADBANR0mrwoirQa+i7gnFEwMRceXgFtyUvPB2NhNim8SwO5mDCisLvblrgJIBxIPCH4/6Pf4MN30JprRw1VRIigorKLHURi9AhttBIkgIITZ2zJxXAElmyVRrQNbqhK0H3YCYeUzkBGCnxvDnh88PqXcMxoQsMKCH21CTaUwN5WCOkxitljAQ2K90NdM7TqLSXMRjhuvLSV31PZcf82C5wwESYNg3eWwdxxhFwOQrsrYNs+mD9drMcv1sGEIXDFAthbBSu3w8lT4OPVkJsOZ8wU0qtqgHW7ZV11IyzZCKfPAKddPjM4FxZtgBMnSrR+7lgYWQhfrBeynjkKapqgvBbmjIOIhvb+Cv5VWkVrBM5JhZOcsNgN23xwbWbcqxYXBTh5iXP5Q+BLPg70ktOQMDTcbEAj1CWB3IgLB+OSbheUngWDh8OGlXDcArA5YN0yOOls2LK2n4ivtho8no4pDWnp0qaoJgmRTDtSUvqvCe2huEyzc8CepFAlHIaSnV1dv8NHi/Al9G2U/EgC43Ng7kB4YnVc4lOBoVixoKKhUUNQYuZEcGCgmgADsWBFZSAWhmHFgYFiPNgwMAQL5QRoJUwBZlIwsAsf3Wp4tUiP5cVU9DnyoZ/5EdnvdwUB/Lg7hV0CeuOrMGEa6JhaEiZ8kAgNsTRlMsB5M2F/PYzRc0raOzUkgUMjvmAIPl0Nk4fD4ByRllqMMHawEN/Bs4hIpu+MOMVHAyHYWS6k0wUafLNdTKdxRUJin62FkgoYkifLi/KkSOKBOnhvOXz/OPhsjRCbwyrHVNMkxJXqgOMnwFebhTgnDIEZo4Ush+aLxTlvEuRlwNZSWF8i7tiRhbLOH4RpI8EXgF0H5O+QfLx7q3ipEV5vEq9OUINrkiA9AAMqGdgwdykMdWhws4kg9VjoWFhVwUg6J1HNa0m3CzIYxeJvrzSkGvo5aaKmCuprO1YrcaXBkGFQvDX5/eUN6FvyensDxFgYjNL3ry8YPrpvzXA3rwefL1q7VFFg9HjJd6zqm6rtiOHxVfDP9dAQ38qyovJ9MnFgoIUQFQRwE6EEL6Ow8w71BA4OgFCAGRXIxMgkHATRSMXA29RzMdl8RmMCT1BH+jGgMJF0zqCQYThxE2Ip1XxGBc2HUNIvut8BDCOl3/b7XUSYEAHiZ+hrRPAnYsXbzDB+oIhbdlbIs9nHriuHVs3SG5AAoy8gMbvhA6DRLUSX7ZJChkPyhHwG50JBFmSlyvLcdCEtuxUG5UJhNqR1Kv8e0WDEACGlumapz+b2yv7HDRb3qNsrI3Bzm54QE5b9FGRBUa7EDtt8sl0gJFbf5GEwZbjUmCqrFhJcVSzW4ElTYHuZnFOTW/4aVCHt5jYhv1A4+v+Y2WNQg4CW+KzOgoEsbDgxdUscRlTSsZKBDWOnn8uo12aJhYqCFePB/XnZQxub0TodlYJCOidgY2iCR6tDE89hegZ89Sks/gjyB+pNmPsLjXWwu7jjMrMFZh3XN4HLhCniZkwWgYD0+IuF3d41ry4RmM1wzNy+WYzbN8GBTpLCQUNg+uzk99UdsuwwZyA4O5G6SYXpBTBEt5gVIN8JJw2BSybAeWNgbLa4/mMxNB1OLIKp+TAgpasyNQZNhNiLj3ICWFGxouDEgEV30Le/wsBa3GzDQxZGCjBTgZ81uNGAffhYRistSWhKVeACiniDE7iYIrKxMoUMHmcWDzOTjD4WgFBRuIgi3uJELmMYOdiYTAaPcgx/YybZ9GEC9B1GBA2th4JykUSKzRlUSHPAlv2QYpN0hm/F1blwHVS2wZcbhQTLdH1ubbM8CJ+tFdJRFKhqhK82CUMrCny4StapilhVlfVdc7dW7Yjuc9cBseLq9RjPoo1CiF9tku8rrZYM4vd19c8324ToKurEvaoq8t0NrTC8QEh1X7W4aLNdQmQgx7FZL7thMkrsr7xOjsXlgLW7xPprtwS6Gcg0enZtTCSHW5jDUNKow8NCSrtsM5Q0rmcmU8lDQWEzNTzCSnbqlRTOYxQnUcTNLMStz6bGksX/cCx/4Et20UAYN7W8TxrzdBVnFFYGkcsl7OVeEhWgBwOwbT0s+iC6rHyvGEGh/krc8vtFsbjgzGgFAkWBExfAk48kV7rM7pC2RH2ZGfo8UtMyFlabNHJd8VVy+xoyQoiqL8RXVQFLF8KwkdHPW63wwytleXPfHv4OGJAC//we3PYFvBPTW250Frx4Hty5BPY2QX4KPH22/G32QaoFXFa4awm8vCl6wx8zAC6dACMyodYDZ78CTV1n9RE0KggQRCOAho8IU3GShYld+JiEgyxMHIOTGoJ4ieAhzH4CNNLEMKw046WVMBXdWBQ9YQxp3M1kVlPH7ayjCi82DFzJSP7ARNZTz2Mk0WtPx1hc3M0UimnmBlZThhsLBi6kiPuZSilu7mET4e+881NBPUT7KWG4ffB5op0KesahEV9loyTvePUbrqXTrLQqpv/apk4S4vKYAWV9NxmcjW55taMipuN2qwc2xrhT24mrfb8re7hZt5RG/19WIy+jAU6bDks3Q0NLR8Zq1Wf9+xPvVr7ELcWq6+KQQRZ2HmI+PkLczVKsGLmGqWTH9LfOwsbDLCBAmD+zDAW4lmk8zAJ+xLvU42UgqcygAFPMjefCyhwKSY3pRV3HhxTyC+yM6iRyMVDAlTTwBc30UAsuBhVlUFPZdfnC9/o5R3zpF1BbA7n50WUjRkuprqf/lngO2zHHwow+Wlo+v1iex58S/byqwqlnwxv/BneCQhuzGS796cFamEkjFIK3XoazfiBxQpDjOXYeXP4zeOoRmZEcCnY1wM56OH8MfLgzWkpkwTCJd3+jt4mp88C9X8GBVmj0gssCj54BV02Fd3dAq34cb2yFd4vhDyfAyUO79YX70PiYJtB9EhqwGrdu4WkYUNiBlwgaIitTiBBBQUozLEUhpJdhqO4D8Z1FIRlYeIJiyvTIYIAIb1LKNYzkLAbyHLvwJJmZeBaF5GDlV6xkl66c9hPhFfZyHoO4gCKeZicVRyyP9vDAiBFrMp1XjhIcxY17jjBCYbFCl27plwh0sR/+06wnsnfCDPIZRSZ/YhmfUMI7FPMP1hCJ+eITGMxoMvkjX/EJJXxMCX9hBZPIZTr5XXfaA3yUUskLXWJ5CgoWChnOX7B301KoM8JhKaLS5Tu8XblILMw+3mK7imHxpx3zSIwm+NkNMHdeYkQ2ZDj85o6+C1u0CKxa1pFUFEXI9NwLRQ3cG0wm+P5lcMlPDq1+3sY18PYrHZPMLVa4/ja46rqDdTaTgtksVuQ5F4DBBv/dDrMKYUi6rHdZ4KyR8MUeqNCFCYEwfFMOlfp7TxDWVkCmXYpatiOsyTp/IoRhwEkaBsyAio0UpBeEASM2jDgJoJBOJidxMk5SiaDgwIkJJyHkkU3W4WBEYTIZWFD5FWN4hjkHX39iKumYycWKPUn7wIjCRDJoxE8xHXOMPITYSCMF2Bl4FBSQj4fYsEhv1pwDJxnddIA5mtGfkZk+o30Ii8c3VlRcmHHosSwPYZoJ4j2E2hAqYMdIKiasqCgoBIjg1kJ6wZzD634YTgatBCiN6Vi+m0aaY6qsTCaXdGzcwmz8+rm6sJCKmYEkO8hFqOQFsjgDF8d2sPoUFFzMYizPsZvbaWZZ0mKXjlAxk00qM0nnRPbzKD72Jb+bgB/+9aQ0pM3TLSVFkXZFD/4D/nKXdCCPl97gcMKMOUJ6M+bIsnA4MaLqjFXLYW8JjBobXWazw613CxG/+zo0NtLl7jWaYOBg6XR+xbVCvu2dqPvidg0ExM07aQbMnBMlflca3HaPkPHLz8HGtdKpPhTqOGkw6LmMqWkwYKDEPY+dJw1ofV45z8V74ea5cPIQsf4m5UFROtz1pRAZgN0EZ46AM0ZKXFBVYLArGohLEmbMXMRFmDCxkpXkkM1QhhEmxAY2chqn0kor61iPHTtnczYevGxiE/OZz2AG8S7vUkLySekGFFL0+HoGli4Et5I69uEmmHizI0Die06M+Ingj/PZFgKYUJMm1CMBjQg+vGhoqKik0bMobBBDyE1yIn5IUBVJM6ttkXYYIHl9SeJbv/JZWLiJ0WRgpgwPj1BMGyFysXIOAziTAoaTQop+qG2E2IeHhVTxX8rZqwe2e4MRhYHYmUUWx5PNWFzkYMWOAQUFP2GaCLALNwup4lMqKT9MbggzBsJECMU8FCEiHfz9Vox4CFJKM/4YItpINet6qHupxm07CwEq2cNdjOMFzBR0Ij+VVGYxnlep4iWqeY02dhAhkarnBow4MZOPk3GkcSJpHIudEYRo5QDPJrCPbrBxLTz7ONx8Z1QNqSgSL/vfp2DdSokF7t0tcUGHEwYPhemzYMrMqKW3b4/0r1twVvLHUFkOr78At98TVXMqirROuut/4fuXyjGU7JTEe7MZ8gpg/BQ5jkFFeoduDb5ZKvuYPqtv16N8H9x5Ezz8LIweFy1jZLPD6efCCfOhbC+U7JLjbnOLGMjhEFVrbp4cW3aeWIjt8fY9u/T9twj5nTMaXt4s1t7eRliv+7YV4CeT4bdz4em18OxaaPLDJePh3NF9OqVMMnHh4gmeQEHhFE7mJV7mTM5gOMPZz342sJFxjGMZX7ORIr7mawoowI4dB04GUNgn4guj0UqQBgJcz0pK4lQ50uCgqjSZ/TYRwIoBWxyNaToWAoRxH4XKTg2Ncn2iqqAwjVm8xDN66kFHGDFxJt8nlX5KFUoEOS5JXv9wHUwYBNXNsK6H1kDd4FsnvlRMXMpgBuFgH228xj4yMPMAk5lLNiaULkP5GFzMJ5fLGcLv2MjHVPR4aw7Ezk2M5gwKGIwdU7f0ANPI4AcMZCON3MEmPqcqydu+d1TixomZdKzU6uSSjhV7TK7dXppoI8BzrKesm+oqYTSMqBhiziUPB+ZuftZGlrCHPzKcv2AkrYvlZyGfQdxEHpfjZhMtrMZDMUFqCeuTABUTBpx6AvxAbAzDzjCsDMJEFgrmg/sNdVMuLWGEQ/D8E6JgvOSKjsTjcMJxJ8OxJ4k1125JtQ/mIMvqauG+/5GO7n0hvkgEXn1erKwFZ0etNUWR9IJjjoWZc2W7SESWG2IEOe3HsXMb3PlbOONcmHZM3/MB16+C31wN9zwMU2d0FP84nCK8GTOhb/sOa/D2DhGvHDsITiiCVzdDsz7omQ1w2gjYWAUPLRc3pqpIClMfz6eFFhQUTud0drCDMvYzj3k4SWEvpbhwESBAkABefLhIYxKTMGHChYtWWgn2kUBCaKyhjrMoZBzpbOvkluwrwmispo5zGcR40tkd8xykYGIqGZTjYX9CE8sjj/Wsoo1WnKQyj1M5lbP5mHcPVl0BKUx9DhdyEVd0O5YeFhSkC+EpimhLVvWtwnfCxOdrhm0fgkX3snkbpQtCfyIDMwvI40qGMZV0NKCBADX48BEhBSN52HBgwIDKBFw8yjQuxc9K6rvdbwpGzqOQQTjQdA+2myD1BGghSAQNFybysGFFxYTKNDJ4nOlczHLW0r8ljtZSSYAwlzCev7MaAyoXMIaUGEHKQkq5hqn8guk8yVpaCeDETAFONlKDhyD7aCYbO3MYyJfsIws7lzAuJpmhM8JU8iIqVobyR4xkdLlpFVQs5GEhjwzmg26bRiMoKgpGFAx0b1/2I1qb4U+/A69HVIwOZ8dBVlHilzLTNLF+7rsDPnxbFKF9RX0t/P43Mv0/5XRxY3Y+BoMhvis1HIZNa+EPN0mcbtgIWdbX8muaBmu/gZ//EK67Gb53IbjS+0Y8mqZ31a6MxjHXVYjld+10SW34NGZgCWtQ2yaVWMZmQ50XZhfCacM7xWJVyNZjflk2sBphWAbUtIkgpjUaM22jjRd5kXzyqKOOj/iIgRTSSBMePJgx48NHLbU008xrvIqCSg01NNBAgAANh/B8vst+fsRwbmc8TQTYSAMhIqRgYiQuDtDWJ0L8kHJ+yghuZCxluNlFKzYMXMZQppHJX9lKzVEqbNnIGlawlFM4g3Qy+TN/5wQWsJ5V+PCSQz6zOZ7ZnIAXD5tZzwSmHJmD21AK978jtToPISKV8NPXuA9eu7rjskh/ydd1ODDyRyaQhYUa/DxHCW9TTjkeAkSwY2QSadzIKE4kFwMKQ3BwPSO5kpX4urHNdtHKB1RwEYNYRyOfUckq6tmPh1ZCaGikYGI6GdzIaGaRiYrCMJz8nOH8nDVJ+/l7wm4a+AsruIlZnMpQfITYTh2bqDn4PTup5zYW8TuO5TSG4SeEGQN7aeJnfISHIEspYzGl/JVTqERmIeupYg9NhLq5KzQCHOApgjT4j98XAAAgAElEQVQwlLuxMbRb8pLlxo6VE74NNNYL+a39Bn76S5g4Vdx78Qb7SAQaG2DJZ/DkQ5L8rapdO7oni3174Mar4OIfw0U/hqEjJGYW7xjauydUlMP7b8Hz/xASBnHLNjdGj6e9IHdfjueOG+G9tySOOOd4yMkXQu2JBDVN3MJ11bBlI3z+IXz5eTRto9EHb26F62bCwr1QEqPMDkWkAstf5sO/zgN3ACpa4O+rhPza44CFqfDgAsh1QLoVLAZ49HTwhuCFDfDixg6H1Kz/a8duomptr04Oft3VdiCmxN6ePrg3O6OEVn7DKu5nGi9zHNV4CaGRigkbBm5gVZ+Iby9ubmYNDzKdt5nHfjw49Yn765TyJMUJjCha9/dGP1feNxqitdBbaOZB7iSHXCYwlSxy+CFXcSk/1WN/8t01VHE/d+DAyXgm9+vx9Higk4okfy90hLoz9DfRdYaKQg5WqvFxHWt4h/IOA3gDAcrxsJVmXmMOM8lEQeEEchiKk23duASDaPyV7fybvWyhGXcc8UY9AUpp0/c9lwm4UFA4nhzysVLWj26JMBr/YiPL2M9gXNTjZTt1FJBCjS6p1oCPKWEtVYwiAydmGvGxhybq9WOpx8sv+IRxZOPCQjkt7KGJQaRS1sPDqhGkmtfwsIMifkcmp6Fi71frTSNMgCoih1JXMQaKz0/uO6sIf7mD2in5MPt4EZxkZIkF5veJ5bJpLSz7ErZvjiafG01ClIeKhjp44iH472viZpw+G4aOlIR2s0Wqire2QFmpxB9XLYPSPWJVtaN4K1z7Q7DogXmfr29l2EDOeelCWLlUurRPnCqvomFS3s2qx0X9fjmumiqJ5+3YCrt3QOWB+BLdf66PpiYEOonIVlfAD96AghQhuvIW8AZFEdpeneVAC/z64/hJ6839cz/0FzRgIVWcx2JOIJdxpGHCQCUe1tPA6k6ltJLbbyXns5j5uk7BTYgV1LCcWtoSEZCFQ90Pura+xdUsZgnV+oNSO8QfkKJWeVmwe390u02s5Vou4VKu5Hjmk0cBFqxECNNIA+tZxUs8w2qWM4WZB7sntHYad/azjw2spp46fJ3Ggkbq2chaIkRopmMuqhcvW9hAKq6ONT41DQozYOZwaPbCvlrJ70sS33rd9KE4WcxJDNKlvWE0HmAbd7K5W6sF4BqG8zjTMKLiJ8ylLOe/nYqg9gUKcAtjuI9JqCg0E+B0vmRFHx+Aox0GUsjkVAq4klSO6RL7SxTiQI4QpAE3m6njfer5CA+7iW2Gq6JixoYfb5dKDioGLPo6FQMTOZbdbMBNM4WM4Hi+zzd8xO72+qIms4hJVFXPs/B3JJl2WKzw73c7uju9XrjiPEmZOBSYTHIcBr2GWzAoitRvszeN0STXpd31Gg6LZRnso3X5XcTZf4UTbuq6vGk/PHGidLpOFKkG6TbdEJLRsuUI9WFKLYBrF0pT0c7Y8TH86/zeG5vGwGmHH5wipZXXboeZ46DFDVv3wPjh8EGcmgwqKi7SySQbG3bChGiknjpqCep5kwYM2PXx24OHcAypW7FhxkyECB7aOlRoMWHCqucue2kjFPM5FRU7TlQU/PijJc0MKpwwBnLThL2/Loaa5C3yb13c0hnV+Hidsh5JD2AtDbQQJAMLJlQK6YcZPXJfr6UBH2HsGLFgIK2XLsTfZYRppYa3qOdTnEwikwW4mIuNYZhIR8Wmx/NiyTCCRpgIPkI046cSDztoZgUtrMLDrrh9AFUMHMu5uMiingpW8OFB8lNQOZELcOBiC8sIE+IULsNJOhtYwkSOI41swrFChmDg0BO3e4LRDvYcCWxrGvgawFMLkZjv1EwQMkiVoFAcr4DBIi+ASBBCCcR1jDZQ9Xsu7JdXIlBNYM2Ql9EinQh8DeCrl+8+mqAYwJoG1ky5zloIfI3grUv8fI8UTk2DCXawKPDvWtiSRGzOAMxPgyXN4Ety0uFvgdbq+MSXOw4yiqAm8aoyBlVeHi9MGK6XL86FNdukKFWsu7MdESI0Uk9jDxqKMOGDXdUBYZUUBVwKPoMf3z6vSASswGAVAkBZhGA4SDClWYaWASq0KlCugQUiQ8Dd2gptgFuT/TVrEspYv03+rwCDVBilQmmEbnpux8VRR3zFtLCH3lUzTQRoI0wGcv4p/UhOrYT0mCJ6lOv//jz/MK008zXNLMOAAzM5mCnAQp7eu88GqGgEdSF2I0FqCVBDkHrCtOj1M7qHAxe5DOZ9nuIcrsXGl3gOPjAaXtzYSSFIgGpK2csWVvIRfrxsZQVttLCXLYf9WmBywrBzYMylkDUWzCmSzO5rhMpVsPk5qFgGRgec/Cikj4Sa9bDkpq7ENuFKGHu5/L9sISy7M36PqXaoRph9Jww8Qd5v+Sdseqbn4zXaYOCJMPoiyJ0GtiwhwUgQvPVQvQ52vAb7FydGvIcTqhnyj5FjLZgNjlwwWOWa+BqhbgvsfAv2fgz+/lFZJoxMI0xzwAAz2A3wXgMcCIA/Am1h8CvRUkzjbTDPBa1h+KAxfokmqyKkeU0uFJphmwdWuKHADGekgVGBj5tk39OdMNAs35dtgjfrpUNJ9TYYenzXuK1rAEy9DD67K6lJjdUC9c1QXgNDB0BTK6Q6pSyx0y7vDxmFKtxjhW1hKFBhSQg+CMJ1FkhVhABXhuG/QbjMLOTl1mBvBN4MwtX6Mg8wQIF7fPBzCzzgl1KTt1jgr344wQjHGCAI1GvwuJ9EBb5HHfGV4MaTgP87gtah0kmy1KTCwTwbCwZMKBh1RedQ3cTuEYoZjJmSJxV2Q7iHh7SnbQ1poNoh3AgRLxhzwDYaCIO3GEK6i1UxgnU4mAdAqFHWRbptvgIoYEgFUx6YckC1gRaUzwYrIVhH/PqcGmHceHHj7QfxQCz8ePDjZQrz8NFGIMbnr6DQSA0ushnBVCrZg0aEkUxnByuJEO4gpz5ssOfAsfcK6Rk7CWjsOUJyRQtg5X1QtggGnQQphWKlxCugnTIQCvS8vc4l/eJCgYwR0c+Uftbz5s4CmP17GH0JmFO7DpCOPMgcC8PPgW0vwYp7wBO/OXGfkZYLFjvUlPbsSrVmwIzfwoSrhJw7H2v79R1yuhDf13dAQ/J1MvuMQjPcMgAeOADDrHBFDtxXDlV6t+nT0uDcDHi7Aa7LhzfqYKBFiO2BA11Lx2hAWwRCGuzzQ21IrMZf5cFKt8yqb8iHdxrg7HSoCcJgC5hV2OiB9W1QsgRm/kQ6f8dCNcDsa6Wb/LpXEnJ5+vzw2QrYpd+G+2NCy7sSuTUThRTdgX/4hQR/ZYGSCAxV4bdeyFTg91b4Igg5ChRH4AXdi5KlwHQj3OIFpwL3W8GkQLYi+0SRbVwKnGeCVwPQqsGvLfCWAvsTs6qPOPGlOGDCCNi5T+o/xxp30o+rO21m90g0JmVFZRSpzCaLaWQwBAcZWHBgwISKERUjClYMOHu7NPZxMOyfoDqh7t9QfncP246HYc/JtrX/ggN/OnjkDLgVMs6H/XeCdwcUPQLOqTKAtK2B0hvBuxPyfgn514MxSwiv8SMouw2CnYQRignskyHjHEg9EaxDhAAVvbNtxAuBCmj+HKqfAt+uhK5dfyCAj0W8RjYD2MTXhGJqK0bQaKKGnXippJQwIRbyKqlkEEGjin00UnN4D9BoF2tr/BW6xRSChp1QtVpccJYMyJ4AWeNh7l2QPVkG828L9hw46VEY/j1xHQbdULsR6reLm8yaDtkT5XjNqTDpGrFeF90A/iQKW9tSIRSAYJzB1eqEy+6DXavhs6fpVjJgToXj7oPxP4laow3FcryeGrn2maMhZzKYXTDiPCHtT686suRX7IXFLVAdhJ/kgEEfWybYxSI0KjDMAi0hWNIiy+4fDCkGaOw0kfRrsNkj+/qmFVojQq7THBInVIDBZnAZoNQPJT6wqJBtBIc+idq7DGqKIX9inIlCBpz9EBTNhQ2vidvT16ILYhTxHhjNQpoWJ35bGrsc2TAjHbZ/BO7D+Dy1auBDXJJmIF0Br76sRRPmsSoyWTgQid42VvQu63qbm7ZO95OCEKAVIcDJBrEMl4ZJRn94xIlv3kyYPk5MbpsVtn/Zcb3vEEqRdQcVSUy/npGcTB45WFGJEmZ7bTpN/79GAtWXFCtYhwqpGHupVafaotuasjuuM+WAdQSkzYesS8ExESJ+MKZD6jwo/D00vg+Fv9NLXvnEesz+Ifj3QPm9xIpHMOXDsKfBPlHOQgtCuBW0FiFFQxrYM4WMU2bD7isSIj8LLtIpIp0hWEkjQhA31dSxkxYO9OjmVFBJIZ9sxpJCHhHChEklzDZ8B9VcGlWdOlQ0Uk0jYp2ECeI/3Am/Q06DsZfJwBwOwJbnYeX90LpfXHGKKjGpMZeKlTXu8n6XlScM1QTTbhRLTjFA4074+vew73MhPSJyvLYsGPNDmHUH2DJg1IXi+lz3GCQ0xVRg/lWw/WvYtarr6tyhsH87fPVy9w1eFRUm/BTG/QgMZiHdNQ/B5n9CW5Xu+lWElAuPF4s7e6K4Qo+9Fz698si5Pf0RGXjbB4FhVrg2F/5eJaQ3wCy9x4yKrDcp6LquXqCPKBENmsLwZQs0h8WlmW2Kfj6sdZw7tFTAqufgrAclbtthlwrYXDDjJzDpAmitgraGqPfBYAaTDcwOMNvBZJd9+FvhwIbDS3yDVBiuQpEqbsg9YXCYYIQqVl4LQoDQ8XybNbkWow1gBzJVud7t8Tw7kKMKse6IwJowbAyDBWhMPIZ6xImv3RMyYQRsj1Nppr81ZwpwNgN4iKkMwYGCQhiNanzspJXduKnAQ6MeM/QQoggHtzMubrmhwwJFgYzzoG09FJ8v7tCBd0LameBaAI5p0PQpVDwsJFr0MDimyPrKx8RN2o5gJbQuF5JrWQwtX4GvRMhPtYFjKuTfAPZJ4JwFuT+DfbfRXVsiAyYmczmT+RHZjMGKCxUjoBHCTzP7WcMzrOJJgnGIyYyTafyU6VxFOkMx6v3N/LipYA1L+QslLOo1PnjYYXLK4GxOkZt0/5dCJN6YjhxaRN5v+AdY0mD2HYfWkf1QkDMlapl662HJb2HPh3R4grSIWFPr/w72bJhxiwhtJl4Fu/4rhI4Cg8bB2OMgJROq98C6j8HdCClZsnzuhZCaBYVjZf22pfI9abkwYgZYbDD2eNi8CAJxYoiuoTDp5xLLiwRh3eOw6i+dRCwaBFpgzwdCcme9Ii7kIWfAsLPFTXu4EUGsNBCC8uuWiFERl+YEO1QFYKdP3Jc/zoFcE6x1S6wvHjxh2e/5GbCxDbZ6YY0bjkuFUp9Usa8NSjeMkD7AhzT5fgA0WPeyxPnGnx+/zquigCVFXonUiw70FCLpJ/g0ON8EdgX+FYAyDV4Pwo/MYuU965fOPhvDUBEza2gBXgrCRSYhRq8mApe3g3CpCao1+CgoJPekHy4xwylG2BaBlwOJzeX4FojvyzUizAlF4JsNif1Oh4IxpPIAkw+SXiMBnqWElyllD27aCHeIFQIcSzY3MwaOFPEBqFao+ju0LJH3lY+Lq9KQAuEGKL8HvNtlXf2b4JgMlkKxGGOJTwvKtsqfIVBJF0LzbALfbhj5Bpjzxao0pkEovmpLAwo5hlzGU8M2qthIKxWYcVLECRQyk5O4Czc1bKTj4GTEygn8jjncQBAvxXxINZsxYmEgsxjEXM7jn3zAdWznPb7VzJr04ZA3QwaRkE9EJbGkF4tIEHa8IsTjKjqSR6lDhZHfB3uukPS+L+TV3fWLBGDH6+JmdORB2ggRmLTul/MdOQtsKdBQAcdfBnnD4M17wZ4CWQPB7pIYnj0FzHqsyZkOl98PLXVwoBjmXw15Q+Gjv3e1/IacDmlD5bsadsPmZ3tWblYsh+I3YdqvxU035jLY9Y64cg8n9vjgmWoZPEv98HS1/P1bJeSb4alqcVG2hOGvFSJIKfHBanf3A25rBB6sgHE2IbSgBo9XwUwnpBnl8wcC8vJFhPjMakci9TTAB7eKxTZqQT93fT5MqNDgf3WxSXtofnEIvg51bKXxQRw9x5IQLA9BmgL3WWX7D0LwWUiGs/ZLUxwR4YtR/44kYmRH9ApazNLfddEqSHXA2OFQs7H3z/UVCnARgxlBit6BIcz9bOMRinssPGuIUx/0sCNYD57N0fe+XUJGFid4toEvxjz27Rb3kOoQ0uqyr14SotvWinVpzhfiNHRPfBGCLOVB1vEvathGgJaDruEU8vkeTzOKMxnPBWzlLUIxgpXhzOcYfkGANj7gerbzNiFdc2wljbncxHHczMncTRWbaCSOCyAODMgk3N+fPJk1MRqva6sS9WZPaNkvCsRvg/gsLlF9KorEc0o/g3Av4oaWMiE6R564wPKmi3pSi8Ci5yUWZNb7qk0/CwwmqN4r66afCSv+o1t6OkbNgYwCIUivG/xtcOo1sOQlaIuZiBksMGieuGM1DcqXQmsv+bZaWKzXiVeD2Qk5k8A1BOo29/y5Q0VbBPb4o/8v0f+/tg3oZCXVhOCjBOOku33yiv2exZ2KbfSWG9iwB968Gk78LUz9ITiyvz1vQ29o0WBlSNILOp9Wovq0AODRYHlYXJ3tyzojlgiTwBEjPrsVrrkoGjtz2KG86vASnw0Ds8k6qNDcQxuvsK/XauuZmPVmJUcQ4SYIxcQxIh55Afj3gRYzQ460AWEZTJROfv9EEPFDUFf2KSaRmPeABnbTQNdisK1UsoU3GMGppDEIM46DxGfAwmR+hIVU1vAM2/hPhxw8H018w2MMZz4DmMYYvsdyHun10A3Ahakyef7QLc9GvyBjVDRe11revbXXjrAPmnbLYH6kByBHHqQM0o8jIBZoRi/dEQwWCOvXX1Hk84peX3TOBTD+RFmfWSjxod7OKW8oFIyEHz0g18BsBW+rJM/HwuKCtOGyPy0saR89pXO0o6lEBEVmp0xI0kckR3zuGqjZ3nV5S1X0OnzX0FIBH94Gm/4DUy6BYfMgfaBYgvF+s/a4khaGkB+8zdByAMrXHd74XrUG/w4eugPHDTwfOCyOoCNGfOEwfPo1lOhlcVLsUNTHhtSJwoKBjJjCzxV4aegly1EFZpKJ+Ujn7kW8HckNLeoyCkuH6uiqSPR9TwOUYgZjhghqDC5Jm1At4la1FPX5UBXUgy8fTUQIoWLSE90FTnLJZwphguzms46J5zraqKWUpRQykyHMYxVPHLQI438vTLfCAge80iK/1Yl2EcAt98AgE4wyQ1MEPm9L4nlRVMkna7+W3joZKHpDWx9LjR0qbFlCCCCuwHkP9T6YKwpY0qPvTQ4hvsET4Izr4IWboWwLTD0djrsk3g46vvW6Yd8meO7G6LWKhCU2GAuzS9SlINapu4KEEGiRvD5XkcQxUwoT+1w7lj8Ba17sujwSFtdhklBIx8hwDAzHQB5gQxoWNRCmnDClRDiA1tky7BVGDBRiZBwGhqCQgkj89hNiGyFK6JCZHQ5A6TIoWwnOHMge8X/YO+/wOMpr/39mthd1q1u25d67sY0LBmzj0LuplxYgCQkEEgiQchMSSA+EEgglhN5NswFjqo2Ne+9Fli3JtnrZ3mZ+f5yRtZJVdmXLN/f53e/z7CPt7sy7s7Mz7/c953zPOZA9BDL6gquXiFlAVLihJvDVQGMFNJQL6XkqRdzS0/UnTxRZ9VD046QRXygC2/dBZhpMGglbdkPpIZJuqZoMYuitrDtbmxY+7WEAKZxP4cl3deqdOKmTrbphzoT0eZBxvqg3LdkibFGaO6KrSakRFVTS6Ushk8hjFKkUYsWNGRspFBids1vDSS8cZBDFTyNl7YwKoBuWpE4aRUY37Y4JRwcORGB/BDYHYaQdznK1KJ9X+GGUTcIoyd0visjpmxH1J2aVRHpKZdrFtWd1tVR2aVZuJv0RhirR6pCFVFO1pC2MOqO1gCIWhZAfCodA+Q4hWF8D7F4Jp18HAyfA3jVgc0nsydPGZW62iWsVhHTaq27THrRIa/etNSW57xfyyOM4oZCCnctwcj1mRhjEFD9takAIjVqi7CDIIgK8iE7XblATA3HyPeycj4neiDSx+bePoFFFiC/x8xgR1tFqftCiYgE2HYJ9hjReUWh17eiiUz+xMDqT6JEuxlbFm4SewLZmwxWu0bkvNJkxO8dJj5KeNglGDpJYn9sJW7/suc8KEOUgPqYYxawH4GYoqazpoI1JIQ5+yygG9wgddzGZ6UnP1u3DPgj6/hnS50rKRcwD4TLJ3YvWg+YFLSiiFuewLoez4GACNzOZ75NOXyL48VFDiCaiBA2F57GQYm9mooQ7JbMwPnT0o9t3hSZN9AL1GhQbz1cHYHcYhtnkLG9oG+7SNCnMHF+oOhyWLgntItFFz4nu1Nj82UksuiIB2PehlCZLBjVb5Lwc2Aw7V8DVD4G/Ecq2QW25vAei0vz8eUlpGDMHNi2BJc+KoGXBH+DMG+QRi8GaD4Uc23onjia1K+0n+beLtpN4T5zrro4gjRQewsn1iIUH8puHjb8mZAp1YKIIld6oFBJkQRfEp2BlBqk8jJkxKJiMuHlz+y8TYEGlEAfXYGU6Hu4lyDscE9DKTYP8dCitFkLq00s6F9R5IS8DUhxQXgtWMxRmQnkd1HZjQWDpA2nzwTlFyCe0Axpeh+AmWt0Hih3ccyH1HDAXyvFGDoL3S/Asau3VMudB2qXgnCYeKc0HwR3geR8C6+K2ywH3WeCaAeYCOUehXdD4lrFd8hPnSSe+YEjifRNHwNptPftZEXQWc5gL6Y0NE7nY+T1j+DVb2UYjQWKYUcjAylR68QMGcSq9qDe6J3eZxJ4oFAvJ15bpBtQU6PMgZJwHxKD+fVGK+rdKtRjNsCoVVZLvEyC+UVzJHH6HjsZKHmcbb9NIORH8aEQZyFwu55Vj9osSJEYUFTMmOo5DWo2uEDFCRt+/zhHRYW1A0p02BiFdhUyTvJ5llBQdaYMVgbjbMRKWZrRt++U1d2/QdQh7WuJ1FpdUxoh1YfXFW4knCqoqJcg6Q9hr/JZ2iAVgzZ8kNy9p6OBvgld+Dq4MIw7kkY7x8a7TtR/C9qVgsUOgiaPtcjZ8AjuWgT1F9vUb78Uj4m8pk6aaJJE9EZhs8js0H2ewo0VKT0HBwdU4uREFOxo+wnxKiCXEKEMnjIobE8VYGI+FcZjoS4hP0Djc6chmxpHKk5gZhqQF7SLI+0RYi0YjqtEkzc5FmBiEiX6k8lc0agnzectAWW64ZgZsPgBVTZDqgHH9ID8DXvgabp0NX26D6iZp4DppAJw+Ah79OC5dIgFYB0Ph02DJBd+3oIeFiFIvgIrvg6/ZelEg4wbI+ZUQYmiPhFfs44UEvZ+1EJ+aCvmPgPNU8C+Tbc15kHYRxGpbE59jKuT8HMIlEN4vhUDSroSU86BsPgSTFz2dVOJLccHmXWAxy5yyfjv0dN2LhRzicqqYSx4qCmeQy1gyKMFLIxHsmMjDTiFO7KhUEOA+NnEdxcwmL8FP6WKFbu5lkF8PwzUa0uYIsXm+hZLbINJeTEWV+F8XsOBiNFdixc06nuNzftVKtSkjmVrF9prhp4YAdaRRRBq9qaA9laRCBgMAhSYOEU4gPhIBlhnzaEyHT+J2WdiZ2j3YTn7ZUWitY0/OHCGfWBcFsF35XRxtGyRiyJkdkmjeGfxVEr+xpgj5uvI5LndBNAyNcWXMtDaEr+vi3mwPQZ88OkKoQUqkpfUTV2iiKlh7Zst5iIUTLPd24qDgws6lKNjRieHnMbz8roMYngWVHCyMJ8pOOpMZihX530dJL8wSmrjL2K/FcgryLgFeJY0nsXAqKoW4+Tn1bERvLhid4QZPEL7YJgu2OaOlRU+qEywmqGwU4jOpcNEkqPeJRagqiROfYoXsn0jpw7JrILAB0ME2BPq8Adk/g8Ba0Dxi7aVeAOE9UHa1pGGhCMmpTtDilKzWfuA+HWoeheo/IZau2ahM1cYd7vsCSs+DSLkU8MAEaZdA4TPgOrNbxHfSFBxWC5w9A267CiaOhMljYNKonv/cakL8hPUs5jAh44LMwsYksphNHtPJZgBuLCisp55bWM3rHOBbao7K9tuFHmlxv5jcdHwqFSGkk0F89gGS9wfQ+FUHpIesmGxFXQ5nwYGbbEDnMBuOIT0FE4VMatfd6aWSQ6zDhJWBzEZtp4i4kyz6MR0djQN8c8z4JwLNFY66RN2OllhqShG4Cjrf3mQ3lKBdsFksrkWR2dl1DpYrH9xdqL58R0T1CGIZFU7nJN7KySHsEem2bpRCyTulpVtFZ+g1QqrkgBB9QxfVhcQ7mBTsE8A1t/33FFIxIdeAToAQH3ciXImgUUGID4nR+XHaOBMbsw0vxwGa+BlRtnOs21wjyhY8PIBOIwoKVqZg4/SWTSrqJGZ04+kwMBciMSG9iloIReFwfUsIJaqB2y6uzmTWSOZCcM8G3zeGtZUiRBatFKvMPlbcoGDUAj4Mln7gngOmTEAHrQGibeaiWJOI9lwzwTFOSBNjf61NlR7NI2Sqh0TFrtohtFuI1JybxJeJ+1oJbWUBso2tG6CDfq+dIhyBtxYDCmzdI1Zfn3xoJMxzlJCBBR1Y1Un7i3g0EuFf7CPDEFZ0tt92mvgvVnIehZxLAQNw48aCjo6XKKX4+JwjfEDF0Yaz71GOEzM6evvdImL1Ej8zp4NjmJQYay8XzpIH6WedJMl7fEykE7dhyjRwjOhytChBAjQACr0YignLUXWmipkBzGY0V7S7b4ww6/k3A5nLcC5mL0vYyUI0Y38rLiZyMwWMp54SdvB+wt+yLWyKuDvrYjL9a7REiAZZodAMKwNSJMOpgkOBmpjsl6LK/9HqzWKZpBSJxVc0S8iwI6QXQ68EVm7+Ko7Wv0rtKxVfOisSXXQaOLI7fh+ETPZ/DIXTxH048HxJCm84NuWkx6E6JD3m6FlvM6vqMSj5CIZfLa7LglOlaHbVho7HNNth8IRyHC4AACAASURBVKVCkLouOZVNHQmkBPYxMgd6P07gmBVABetAMTx8zXXATcSVH4ugG4ljClZMDASWHfv9koIVO5cBDnR0Qiwi2kW3kQiribINK9MAOzbOIsi7SBH7MLy4FOwWCEZg7xGZWKMxIboP1hqkF4OXlrb0HYolES+15IEpC1K+IwQVD3Oh/P6mZvd1FKr/Ji7LgkchvBeaFkLTu0JU8ZZwpAyqHoDs+6Hvu+BfDU3vgGcxxNqkEqkpEjd0zwFrHyFJ1SXHlXDMuM2hJ7KRehYoAwEvaGtA72bunabDtj0wb7pczwu/ls7nD3Sj1Yzsl3iQsIYQz1PCK5SSigU7KjoiHG5uQxSP9dSznk7iCuEjUlTa2lssuuxrpeiz1uxSU8QnXnifFI0+GfleoVJxE5hSIPU0+fxI/CRrhpTJ0OcBuVi7aEoaxscuFtGHqYzlajSilLMKE1aKmMJQzqOeUqy4292/hC/5lkeZwd2cz5MM4TyOsBETVvoynQGcSRgfX/EgNezq1le2APNTWwyKI1GojQm5KUA/C/S3QqmRVnRxCmwLwbognOuWikh1MVjYuB+9bKnU4VTNMOYWaSNUv/vYDzU7pMOAuwurEKRgdMQnbsn0AdDvLNj+Eu1OoFkjpJB0l5U5dEk+H/Ffkr+XORimPQBf/7TrdAFrqpCv56CcMOdQ8VwE98siLuaR8VW3iKHMqfJci0hsRzEUeIpFtrUXQ7BEuoY4h0HdwmM/s3wpVCyHvnPAnQ+n3AOf/0jSRtpCMcHgy6D/2XK/hBph2wudJujbJ0HW7UY4aQx4PhSDxD0PGt+QYVLng3eRbJ/xXVCNpijRw4AKztNkbiUCjW9DaHMDETZhZiRgwc39SJn1ReidzQudQNyhE1BQ0AkT5mu6yr7W8RBlL1amoaBgZjgKLvRm6yOmgS9OMBKNc8+H44kmJo82UAy2b9sUugWGEjywTgQqxxxgGMIHWp6HtkDZVeCaJcKVrO9Dxo1Q82eoe46Wki0xaHgV/N9CyrmQehHkPwyZ2+HwTyGwxjhAJ+Q+CGmXg3cJNC6AaJUo1/P+1Om56wyJWXwO0FaAXgbt9BdNClv3wk6jQEc0BqSPhtwZHLVWmvbA4U85bomjqw8Unm1I+IFgJZS9T1gLU5NMx8KOoPmg+kUp9Ky6oOg3kDIDvCtFNWnrIyXHnCPBu0qsQksPF2jzbwbvakg9Q6y6gf+G2nflQjFlyLFmnC2TVuNiiQd2Cp11PEc6fRjFFUzldqMZlEaQBrbwJqt4gvN4wiC/1r9ZjBDL+DMB6jiF7zOGqxjPdQBECFDJVpbzV7bzXic3XudwqdDLBE/Ww/czJO7n08CtSrnAXWFo1GBHGEbbhADf98JAiwhi3vfAjelg9oaIbH4a+p4pCeLZo2HOU7DyQajaKAINk1UswpE3SFWRaKAlebgj1GyRCi/5U4Qwp/1aLvXSzyDcCChCRAVTpWVP1nCR/LdtidQWDftg1e+lH6AtHYZcJse25TnpJhGsE0IzWSWJPLWvlGQrmiXdDj67TVYLrpFSGzZaDxlnymuaXwgo2iTXrCkFonXg2wqOwWArkHzQmvdl/3CljGFytX+soXo51qzh4sYddLEUTVj/KNRuk/OomOS8D74Ext8hBK1FYcersgDpBJEDYlygiXERPQSmbPHQNb0jQ7vngH+paDIUJzQ8B1n3ACaw9BVNRu3DoKYJiR65O0Kg/hlsnI5KASb6k8Y/cbCWIG8YIpdSEi9FAiZ6o5JjPItiohgb53axl4Iap4RQyUTB2UJ8xwWFPGZhoxelvNH+JrE6UYKH90Pt30lIyRyrlx/CswhsIyDvQcj+ubhLQ/HGii6CldpHof4F+ZHy/wrZ90qMUA+CbSikXyGEd/jHRowPGTeRlKMOkBjxVYM6A/CDthL0TrwUiaBVl19Xbyg8B+zZkD4Syt6DI58d15cCwJoJ+WeBIwfSR0Hjdji0uHX37ONF3QKZCPJ+KGW/Mi+SRzP0ENQvktqZ/f/Z88QXrYeDv4T+mdKdIe0sITfdUHKiiFVYdi+EK8A9tcshA9TxCXezkZfIYQRWXASop4rtVLOdKGE+4PuYsRFoZyUcwcdKHmcH75PPWFIpJEaUeko4wmZ8x9lqyKtBdQwuSRFLb28YJjvEpbnUL9bcdCcMtwrRNRj3bWVMyp6dnwL7wsY69NAKWPswnPorccsVzZJi0I37xfKwuCC1t1xT3nLY/Q6Mu63zmFWgRib42UOl43hqPyHUxlJxgyomca2m9BYX38EvxcU69rYuvrkOu94AR5Z0XrBniusz/xQhvVCj3EMmm1ib1tSW4/Q1qw41CJUj5FvYUuAgAkSqJf9TDwkBqw5wDARrtmwX2GMQpAXMaUJ+eif3VvkyWHofnPYnIbhBF0kvw6YDQowmG7h7i0WomMRVv/d96R/YRfPcWJVYeMQgbDgOTO15i1WZLz3vQ3gf+L8BS6GIFqNVENwoWo7068DSG0L1y2nidtz8BjPDUHBiZQZWTiVGOWGWEuQtwixPyApUyUE5mhbhIIUHSWyBHx+lttLVtG3GTTaTkc6apVhIoZHdpDEEH2X0YiIRfNSwmkZ20IeLOh4sckCsPfdssA2HULx3ziTxtqN9QVVQHKD75XvpYQhugKZFkDdVFt/NUKyA0qLy1BrB8xFk3gSWAnlfD4rnQbGLa7SZ9DCB6zQjhtg9JER8WgmoecbWJ7qBc8UncOQLcPeHMxaduHHrN8GyK6RaxWkLuu0L7hSaH8ofhKZlkHmBxM1MKRLkDZVKz7yGhRD1oNT8G92/HsW7vPWl3viVxEhCJa0nDs0PtW9AUz741rT+3HAZVD6DrJjauLa8K2H35ZA1H1KmG2SrSy1Q3xqofVvSG8wZcOQxmbiind+0UYKUs5rydpWZUJtv5JAdbj+uqKPRwAEaOCBxhqF9YddBCcwfJ6LAm01i9dUaZf0ORuR1jybPX22Uup5+Td7DeO/lRmmFVmXUzUWLwobHhTAm/FhcmfZ0sMfFNrSoWHHf/EKurUQKAex5V6qXnPIzscrMDsgaJg8QKytq5OMtvVdEM6NuPrYNTVvEwtJ5oaFErMXcCTK2K08e8dANd6X3kKQ+NLu5fdvBXgShw4AuVp7mFze5Z40UQk/NlP9NKWL1KSax7vQoBPbJfnoImlZ1fKx6DHa+BsFamPJzyJ0olmjOmDbbaULM21+SRUiiTXMNV3er50atBiwyH4Pcms2C5ua/eigu48gkU4UeAYgR5F0ibMbJDdi5DBPFgAkz/TDRFzsXE2ENfp4mxEL09vQABhRctBYhJS6qby206zxkkss0zKTgIBcr6TjpjY8K8pjFARZgJZ0C5uBlv+Fp6YR8NS/U/EUUlEUvQOM7EKmQvDv7aBHQVf0a0CTmlv8XUV8Gt4EeEHM680YIbpfVRjOc0yHzVgisElepooJjMjgmQN2zLWQa2S/WZvrl8n+0VnIJU84W0Us3kViMbzooBUA90Bf0E9oXUhP/fdR7gpNUdaMmpVcmq+bqEScaekiaujZ+blRHsQIxowSZzLIpTrh+5lM0+iAQhA+WSiUb0KWJbc1Lx44b84il2B4C26H0jo4OSIpYVzzUooACcb/qIY5e5NFaKPtl9793PIb2kYTnwwkIk9wOuO1C+MWzUHsi3DVCahVxHHqkjbOgMu55OO4e9+nga8u9UT+s+zuUfSlWSd4pYlXpGngPQ9lXQmRNB8TdmciCKhaCTU9D+XIYcK50RnDlirsv4oP6PSJWOfCZWD/RIGx6Ukiscm3nY2sR2PeBWKuF08SKyhwqFqBiks8O1kL9XjiyWtygjaUcdVnFGsFnqOi8cb9f2CjH5t8O0QaxAPV23HqelXH7dBFf1GPyPSvXi0u57xyJe1rc4olpLg6+/2NxDydRsSh6GNxng+sMmXO1RsAk6nrVLTnQxMC/HFIuBFQJQ4W2ykOZD2lXGKHNGsm5Ng6aGHvx8Cv8/Asb52DnQiyMQyENFTdWZmFhEkHewcMv0OioCLce958HP/9AI0FiN6DR1GVVGBU7YRow40RHw4QNBzmYcZPLdGOcqKG0VlCxomBG7yiP1rcMyv4Lsn4A6VeLxa+HIHxQcvOav5ceENM5ZZ7E5FDktcA6qHnECKgaiFbJaiPjJpmn0MWtWvsY1P6Do7HPSDkcuQey74GcX4txENoNR34GqZd0rFzvAu3IsNo5kacBdlDGi5tT+6Rbn9U5XH1gzpdQuxq+ueZYV6ctGzLGiMsyEP9lFcgYBZZUqFljKMziYHbDGR8J8X02FyLtTLaKCZyFkDoUHPmABr4yqN8M4eQqYigK5GZCZirUNkJVPWSnw6zxUJgDew7CohWy4M7JkO0qqsHTwz1WjxuKApOHw8QhEArDwm+F6Ib3hQlDYPVOseIcVpgyQr50biZ8sQ62lUJxPnxnMgRCcOYE+PFjkJsBM8fKVbhkLezpomr/SYci7keTHbGGAq3b6Yz9AZz5uJybsq9hwTlCZF3BZJNxFZMkindYIi2h27ODY7YJKWtROeZYuBtj9TAUk8QyTRb5/tFA17mTHQ3lkvlWTReVZqQMbKPAOVXmzlgdBDcDmpCjOVeKj8TqILxbDBPXLOF27+cQ64SPFNKwMA47l2PnPFQKUFDRiRHgFZq4rV3Lz8bZZLAABRsatdQyhyjHGTdqB3ZyKWAObvpSyTdYSMFKKhE8eDlIJqOJEaKSpWQxkTSGcIgl1LO584EVq1h1zUreWAPoba93s1EXOEVaV5kiULu7fTe4YpcQkSsH7A6o2WPUJW7HAFJTDddmTBbtul+0FbpuuFaTQ2Kuzn2gDgX9MOj7k/6ME4OsSTDjFVhzB5TEFZ9VTDD8bsgcC5/PA39F8mMXXQRjHwBrhlTCUMxgdkHNKlhzO3gSl4mPGgA3XwjlVdDkg399KBbfoD6iNJ49GZZuhOICuPE8qG6QSjZ/exVqEuxy8j+GaBRW74Dpo+Cq2fDXN6DeC0P6yOS/66D0m7rvavjXx3CoBm67CO57Gm6/BFZul8ok2UYrJV2HTXthQAF87wL42VNtAsD/0zDIrosYU9KIhTrvRxf/+QZcFpiQDzaTTAtbq6CyXY7toWPuCegxiHiS0Yd0PJRPhCzxCG2RR1t4Pzr2tcgBaHghwc+ikTBfEWY5fp7Fzc+xcz4KZuxcQICXCPPZMfvFOIyOFwUbCi5MFPYI8QWppISXyeN0ovio5OtW79fTIssvZxHltGlg3BH0cGurrV1EpfJKrBZGjICcQljQgWpfD0L0CPQfBCMnw0uduMq1ptYJ8BAXW0weibk6p4KSBXo9/3N5sooqK+/2YiqqVYLy3S0s7dkL+16A6hVCnKoN+l8Dw+6EAdfDxl+S6Go5K0044MNlsP+QGEcHjggBZqfDPddCXhZcfDosWg6frYE7r4CZ42BBO2rh/xgoiBx6VB/IShUWVxSx+krauBuO1MHiVWCxiJWXlwnpbvhkldQMnHeKbBeOQt9cyM+C7LSWPKP/wzFIt8O1o2BQJozMgVsWwYITGnL4PySPCFHW4+EuzAzAwhgUUrEwqV3i0ygnxkFUsgAbVqYR4iN6puYr1LAqAbV0m3lNUaDPYKiqkA4cqZmQkg6HSqH/MOg3FMpLYM8mKW2X3w9cqVDQDzZ+A3VVMHiMPM8rQjwQFhg2AXKLoLoCtq2R4ufFw6HfEEjLBLtR/i+/LwwdDw01sHWVlBvsASRGY2akcHhzcf9Wbzi7eHSjX9zJRv1G2PZHqFoG3hJo2gG7nwTfQVGENlfCTwDfboFPV8L3LoYfXAp2K8yZDHfMh8kjpDC32QTpKeIG1TSoamgxgv5j0Tsb7rgUSg6L67KzdUA4Kgm0zQIKzVAemFRZwKiqWIZ3Xwn1Htiw5/8IrwtUeOC2T+DOJRCIgOlkNg+xOiGrH+QOgZTczgU9NjdkFUPeUNnHntpxWobVCZl9Zdte/cGR3nHM1GSB9EI5hoyinovZdwMxKojSku+p0n4nCY1aQnyBjo6Cgo3zMNGvx44rip9YstWQdODUeTDRqBBzxsVijQ0cBRfeLO9feBMMnQApGXDbgzBqsiyMLRYoHgrzb5O6uCMmS4EFi1VILxqGC78rxJbfF665S8hzxCmyTVYuXH2X/D/tbDjt/BN8RlqQmKyoBsgWP7oexwEWzsHB7XRmaUX4hgC/oVttck8mVAvYesnD7JR0CHS5wZJQhDpssGqbeP1+foNYgLPGw/LNsL0Ezj5Vck73HITxQ+BQNQzvB592YuX/Z0ABsyqByVH9hajMKkwaBmMGCoPvq4D9h6VMj67LIxyBuibx/f7XPHnudiDnVoWMFOht5DZ1kVDfPmwopB19JmWluu8COZEwGacsFAOnGZwW8EfA346GQAFcVrCbhdh87bj/wjHZv7MyiwriFnVYZO3hCcvfbkFRYcB0mP1jKBgl5OOvh43vwldPgC9ODKOYYOR3YNYPIWegbBuLQkMFLH8WVsYl7SsqDJ4FZ9wB+cNEvarFpEns6ldg6VOt64VmD4DZd8k+NheEg7DvG1jyFzjSM2avghMwJ5Qvp9LLUHsCaMToKNyiEeBVHMxHpRAzQ3FzP03ci047yfytYEIh3RC29ORcqsOm5TD7Utj0jVhvrzwMM88TMjOZZCEz8hSoLINICBa/Do3GtXDOtbBjPXz2NthdYjGGg7B/O+T2EQsuuwAyc6B8Hyx5U94fNgEGjYFso/5t0A/jZsIXC1o6hZxAJER8+kFQBgF2WnGc/HizUDoxHDXq6bYL8mQhYwwM+SH0miTxPS0MKOAqAt+BLnePx8AiuHiW7L5klVh1C7+B82ZIKOyLtdDohTeWwA3nwV1XwZ4ysRSTg4INFxbsaEQJ4Wu32Wsy40lJsg5cC+VV8PBb4pp89TMJYGo6eAOwYKls4w2IBffoOyJiCUXk/wYv/P1tEb3UNcEX66GsGv78GgzrB5+vkzG6kd5gZjJOfmdIxW2EeIogj3f7LJxInFoEV42AT0vETVmYAkd88Nga+KK0hcBcFrh6JFwyDNJtUBeEl7fA2zuENBNFlgN+MBFmFkGqTbrUrz8Mf1sF+7sTPy6eAtc+A/XlsPiP0sF74DQ4/Ufg7gUL7oGIYVEUjIArHoPqEvj4Qdk2rQD6TxUCjHcR9CqG+Y9C0AOf/gW81ZCaK5+naa0nurQCuOpJsfZW/BtqSoRYp98MvQbA89cIuZ5gWJiMm3sJ8RlhlhFjPxqNSEBSA1QUHJjoj4s7sDAaAI1KwqzocNwom/DxGCn8GgUHDq5FJQc/TxJhIzpN6GgomFBwotILM8OwcgYqOTTyvZYi1T2Fkh3Sp3HKXPA2wpGDYoUFvODzwLKFcNCoSRoMCPk1w2Q2fm8MN6UOo6bCnMvhq/cgFBDijN8uaiyULRYIBcHXBDvWQs2R5LpIJIGEiE+ZCKig14CS+x+mDVNUifF1F84imPxPsPcSd2fNKqmoYXbBjNeTHm7tdti8V6g+ZPyeyzbC6m3yG0ZdhegZxXBwJX99JYrF3LIdGcWQkgdlqzpN7bDhYjrfZRwX4CSDCCHW8iaf8Ui3K6AM5jRmcSsL+R2H2isFp+niktzQpgjv5n3HbrvT0ILHtJb/axphYZsJYVeZPI4DMTbh525MDMPJn1Hoos7lSUSOE64YAf0z4MXN0BCEq0fBo2fBZe/AtmoplP+9CXDjGHhqPeyuhSmF8NtZcspfS6J1l0UV4nxzB5Q2QHE63HOqWJ0/Wpyk5WdxwJk/lnZDr9wK1YbAa/MH0p5o0hVine03XBW5g8GdDe/cDevfbhln2T+PHTuzH2T2EeL85umW15c/Z/wTN8NMuhKKxsHz/wXbPpH3FAU8VTD/MRhzAXz9jyS+WGJQcGBlJlbORKeJGBXEKEOjBgij4EAlHzODUckzeuoF8fNMF/U3Y/h5EpU8XNwKOLBxLlZmGeNXARFDAJOBSi+jcouVKFsM9WgPI+gT4jn3Onjt70JQm5bD3CvkPZMZ/B7aVR3v3QIX3QxjpsG4GVC2B9J7iVUX8EFGtuxSuhNmnCvbTJgl5Ll3K8w8XxY+4SD4vceOf4KQmMVXAjhAORX04yu00X3oUWEHU5teZWa3pCJ0FxljIGM0bP097HmWoyfa1ceQsSd5mEAoZgV3LlAvvdNozttDGnv2Ow0ObSKmx4hpuhRmUBTJ3+p/OlSsF8WbO0/k3b5q4i+A0ZzL+fw3m1jISl7BipPD7Og26QFkUEgfJuDq8UZRJxY6jURZhUYteg90d+gUWqylhmQHMnyTAs9thDe3y/OdtfDxlXD+YCG+3ilwwxh4diM8sVbI7uuDMDwbrh8DH+xu3+3ZHo744J7PpWwbCKkOyoTpfcSqbEymUl9GbyieLMRmMkPOoJb3qkskHlc4uoX4avaLlXfaDyDkldf9Da37+jWjoQyaKmHqDeLe3PuN5BC23dbqhOFzheR8tWLpNcNTLaRcPEXItW0rpeOEjh8Nj0E8GahkYGFkB9tqxDiEn6fw8Xe6kqnqePDyK2KU4uJHmChGwY2F4cDwdrbXgRAxDqKfCAlsIljxCXibYLOxWN26SoyMoeOF9PZsFiJb+IJYac3YtVHck4NGw5cLRCRTfQhcKTBgBCx4WoQyVRWw6CUYNApWfwa1lXD4ILzxmLg4dR0qey69KeEYnx4Dbaf83zNQOvjfQKBSktxzT4P9r0CkUSy93udA2hB5v6NxleYx23O5GmShGrE8PSbk2vcycXV6S5L8GgoMv9CIGbph48sQjos5hZqkOacCFExoudlNZqhYJ+QHYE+DftMhayCsfQ48h41vozCImXip5X1+SS3JuWI7wgbeZR/fUs/xWWAnFi5M9EUhFY06NA7CcZKbQhYqfVCwoHEEjQq6HTPZ/xG8a1wfwfqW3y4OnjDsjLtnKjxQUg+jsoUUizOgIAWG94K7pzYfI+S65JFqS5z4FCDbBaNzoCgV3FYhUIvaDTFMSraQ2/C50O+U1u9ZHDIx2eNEHOWb4N17Yc5P4MaX4fAOiQVuWAC1B2i1cq/aJ9bevPvg2uegag9s/hDWvQXVe1pivVaXuDp79Ydb3modAzaZhRhtLkNsc5zEl2KRqcA42WFW0cAV2JiLhfGoFBqiFevRnD0dLzHKibCCIO8RYSOJ5mboePHzOCE+xc752DjTIMAUw3qMouNF4zARthDmS8KsQLd5RUjQFDpWDOo0y6onGf94R6ivhq/jOqbEYqLa3PhN6+02Lm/9PBaFbxcfO96idop0rPtKHs2wWaE4Bd55qkfievFIjPgUUM8AnKBvBm0dx12sGhAhSb8rwZ4jVputF2ROgAl/hnADePbBgbckWdKzW2ptFl0IM16Dxp3gLICUgdCwHWxZLeOanNBvPrj7yevugUJq434PoRpJgN//mpBn/WYpbzbwJrCmQbBaLEB7tnxGslCtkFYEa56GMVeBM6s18cXDZJWbVjUdK6BJ7ycTjMUhJOg5ghkrNlykkE0YPyoWXGSioxPEc0wHczspOMk0avbVEmmn3pwVFxZDeeunnk57EJ5EmBiLg19iZgwypWtE+JoAv0XrFtmbsHIedn6CSgGgoBMgzKsEeRS9Oxe0p0wenSCmt3YxxjSZl+wWWSM5zUJMfdLEKmvGIQ+sPyJt1RLFjD7w0OkigtlRA7UBsHY3/UhR5QB3fAZrXmt/m4q4hGctCqteht1fwfCzYPzF8J37YfI18PZPYFdcro4eg43vQclKGHYmjLtUhC6nXC3kuflDjro0FUWIcfEfxcJri8ZD8tnHA6sKf5gG1QH4zSqDowOE+YIwX6LgMAgpBQUbIm2PouFFp94QVHXnvtGIsRMfO/HzOAqZBrma0Ymg40Wn0RjfuIjOHgA3DYfvfg5H4hK3nWb46ww46IE/ruux2FiPobgQinvDpJGwchMM7S9CuG17Wyu++xVAdT1kpYtLI6+XvN/khbpGGD4AKmthd2mnP0lirs4GI743UkQupiEQe5Hjr9tpdkHmOLAYK8fDRmMsZ295mBxQ9q4QX9QH638GTbshZzpkTQTvPlj3E7CkQf7slqRd1Srk5ewtz6uNVYk923jkwsH3hPj85bDq+zD4VnF76lGo2wCbfgWZ44U8kymYHQtD7R4YcYm4X3xtTOScYZCSD3ljwFcF/c8UBVz5asgdJe7NvJFSI9Jkg5AHYmH6M4V53EMK2eQzDDN2vsebaMQI4eVVfsQhI7ZgxsYELmU6N5JOIToah9jOZzzCPlbEuUQVTuc2xnHhUWHLK/yQsh5Iqk0GKkW4eBwdPz5+iMYRTIzCya9QsOPlVpJVbpqZgZO/EWExfu5FJ4SVedi5HR0/QRKsPJ8k7GZIs7V+nmmHHbUyN9UFxSp8fA181KZOgk7i85fNBD8+BYJRuPZ9OOKV/X83C+YN6MaBe2sg2AQhn5BUQuSiQ32ZqDjXvg7D5sDlj8Ccu2H/6jYLQB2aDgtZblgAA6aJOGbevbB3mahHwz5xc7qzhIA9yZX3ShiqAsWpchIVpY26WEfHj44fkiwvlgyaP6PLKzDTBkMzZLXUegAIREX22y119P8gXA64dC6s3yH/n34KjBwILie8GhPya8bMifDNepg8Chx26JUhucENHvh0BfTOg7OmwUPPgK9jgkpM3FIEegVoSwCflC47IYnsvgOw8mY6Vn3qrUkncAi2/K4lkT0WFKJCgYpFLdtGGmDdTxMft34jrP6hjKvrRtxGE4uv2f2ZMHTY9RE4MoW02lbQaCiHFX+X1WugXm5+PSbuT6tb3osEZN8jW8R9FvFTT4RVvIKKidO5jQx68wWPE8JDjAiNSBK5gsIUruFCfsdmFvEFj2HBwRSu4Tqe4XluoISVR491Pe9wkPWM4mymcyM2OmgtcxJh5QJM9MfDhUSNwtgxNhlFge/AzEiiJJP/YcXOLWhU4ueXR6Xj85E8kQAAIABJREFUAXZgZgo2ribESwlIypNHilWIZ0OlWGKnFIjY5ZkNQmq7a8U6u2okrD0MVT6Ze1Nt4p6sblONSVVaDKF4mFRIs8v+dQGZB/umwWl9u3ngdWVwYC0MmgFFY+X/eFjscg0f7S5vE3JsjrWFvLB9MVTuEsWm2dZCfCariLeayTTsF0uxbCP0GS8uTH89hPxCePN+BiPmweqXW8fyzFZDBXr8xc7/1yMQhXuXy0X1v4z3sFqQeXM/TBktZNbghXU7oKpNyUhFEXLMTBPl+IFDsn8zYVbVSXENc+fF4xOz+Na1eb4+mW/V1eDJ+qN1KSvW9rW24yQ7rh4Tq7KrcROBFhVrrj0Eg9iD16FizEjGxBbhEyLRNr7xuDHqKWMdZaiYGM252HCxkffw0frCSCWPM/gh+1jBm9xJ0HDh7eNbbuM9zuB2DrKBqNGTsJp9VLOPVHKZxg3Jf9cEYWISNq4gfsWkU0uQJ2jd0sWMmVMBsDAHM1PjxhiEQopx7hInPoVMTIwFvNi4FuIsXoV0o9Zirx4hPk8YZvaFob0kLDOtCFZWtFh39UH49dfwtznw7mWwtw6sJuidKorOx9bI8u2SYbJv7xSxGL83HmYUwYFG+Od68Ibh471wxynwxHdEQTo0S9yded1Zy4R9sOSvcN3zcO2zYplV7xNSyh0iZPbefeAzrr9TrpJY4L4VspgzWSQHsGgcrH5VrMdmjDpHHvuWS6qEaoK+E2HQabBzSVx+oA6rXoShZ8KFD0L+cCFgBVGG9h4juXzlG7v9+7SCosCMAphdJKuOL8vh6woIx9lhmTaY2xfGZ8tK5utDsKwCgjGxws7uB6VNMkZUhzf3wBm9YVgmvL0HNiZxjSnAmF5wTjGkWWHFYXEZxBObRYUL+0OB0Qx6c40cdzxMCgzPhOkFMDBNjmtTDSw+ALXHFzM3Mw0rlxBvZGhUEuKJxMMHjV5p0jpnqhDZ0nXyf1aalL6Kx8adMG2cuDcPHIJgWIjOZm1xfe4o6TI1KvG+GP8hUBRwOCV9pIfjn8fAbMRl4qvoKCo4XVKMJBxqLXBqDypF2LkLE0WtXtdpIEI7QeEkkcMgelHMl/zjKOkB1HGQfaxgOHNIJZc6DnYyyomGgo352LkTJe4GiVFiWFqtiU8hEwU3Vs5HbyNaiLI2oaTi1p/uRMGNQjpWLjvmfamX2DOlkfwR+M1SGJIFAzLg4VWi1KyJW7t9UwaXL4DZxaLCDMXgvd3wVVwosyEI++rk8XX866GWRf6T66C0ESbmi8vzLyslf298XuICmVbYtxz+fT3MvlMS060Ow31fB1s/bh1z8zdA30kw6lwjXq0L2a18Ucgp3ioLNkH+CBHOKNIBnJAPNr0Pi//QkhsIQowv3yzHMPYCmHqdvB7ywMENoiQ9UZiSB4UuKGmCPCdcPRQeWA3/2iYnONsBf58pJLKqEhxm+PtAeG03/GmdkNDtY8T6qgnC6b3htEL5MYpSYEIOXPYReBP8MabmwzNnQFUAttXBzSMho51KWGmG+/P8YhFbfVXemhydZrhnAuQ6YW+DuCGuGgIzC+DHS4W0uwUVG1dj43tt7usdhPg3CQtBNA0+/Fom0ebCF8+/CyjHTvKbdsHWPe3HAHSjKIamd+nu/V9HfGmZcM8f4Ok/Qemerrc/kbj0BrDZ4YVHW17L7AW33AODRsCyxfD8I52PYWZ0XBfmEw8XmaiY8bRp8Kqj0cQR7KRi76CkUk9BIQUzk1vdHB0jho6PGKV4mN9usm7yaQshIEiEVfj4EW0VgCLo6Zn2GAoSb4snsbbQEYJ6poPQqg58tl8encEXkaT3t3e0fv1Ad7lB16RCysF1kF4g5cdiEYn/eWtak9mm92HPUlGDWpziKfHWSNpCW1fkzi/g4NmyrdWosO+rldSG9tIfakrgzTshLQ9cRn9Jf73E/yLHZ7G0gs0E9yyHLTVCan+cBj8aDR/th8N+uHIwTMqFqxbD2kqxpC4ZKMKYZYfkNbMKBzxw1zJ4eAbMLITvvC9k+chMIc9EiM+qwg9GC4FetRgO+4S4Xj2rtUQ3osGz2+C1XTC4g7qHngjc/Y18ri8i5PCTcXDLSMh3wf7utQdTSMfMxA7va8UJ5tFIDrgHYvtBzYRYhbxmypP/FRdYxoAe1ohsAkKg5uooVh3TQIjthlg5qAVGQ4d+GtGtoNWCeSiY8iFWBtHdiHosASRGfBmgjDCKmpTASTUW2sBigQFDxeo72di2Xrwy8aivgX88CLf9HPJ7dzWCipnpSBflnkGEIDo6Fo7NQbTgJEbkOCu8JA+VvpgY1PWGgBT+XYOFmaj0JUrpcX++Rh0xdmBiBOBEp3s9vP6/RSQgbs7OoGtCXr5jFyrtbCzE5e+6a/lRaFGx/uqPL7dLUQGlgwjGlhrYWScrDX8UFpXCZYOgfxrUhWBOH1hXBRuqZZuoDp+XiUJpdhGsq5TX9zWK2V7hg0M+Ia96o5O9LYHGxQBZdhiXDa/vFtIDqPTL59004tjtu4rrVfrFMixOFVL3R4VcHd23fVQGoNK/w/cdV4LiBsUBaj74/gquO8DzWyEw1x3g+R24bpWesmomWEaC/0Vw/xh0P0RLheCohpT7heBih4UIFR9Yxsp3d1wBnt9DLMFGOol1Z5iLLJLrOKHVx2x2GHMK9BkIR8phwwrwddzA+Ch0HXILYcAw8bysXgo1huAqMxuyciDgh/FT5f3lnwtBpaTByAnQu58UBdi4CiqMlbhqgkHDYdhYsbhL98CWteLWdLhg7GSw2qCszapb06CxXo7b5e78uBVSMXNKgpZP91DLfvzU04dxrOWtowpOKy4KGUkdB/FQ3WOf3x7MjEdJIjE+zDvYmI+TBwnwEDH2oGBGpTcqBYR4E5EUK0adTjsqhSiYjZhdb3RCRowhCAQI8gxunsHJQ4T4JxrVRsmpQeiEibDwhH/viCaxt+NWliuAwyEXpr+HffyKIvGSYILZ7jabqOqCoZa4iqqC0yHH6W8TjzebZJKOneTavQoUnwFDL5IU222vQ+mXbbZpCAmZNaMuKOc+zSaE1csBJY2t81P8USG1AleL4ihonAdNlzhgdwQnTgu4zEJY8agOJD9WigW+OxLO6WeUlIpJfTvT8SkUxdpr38pUFDAVQegLITnLOCE3xcnRlGrFIdaa9VQIfgB6EKzTIfA64IDgOxBZbQxoB2wQfBeiu44eANHt8jlYDAvyRBIfumyphzhh9VEtFrjxTiGUbRtg+hyYORce/m8pCNAZnC648hbYsQn6DIC5F8Ovb4OGOhg1EW66S4jL5wGrHbZvFOIbNhYuuFrIrt8guOR6uPt6qK2S47jrt7D+WyMHfSzs3y37NVuZ8y6BTavhz/d17zurFGNiYNcbHgeq2c8mPmACl7KbpexlBSbMjOdi+jGRj/g9fqODs4KCCRsmzFhxGhXj3dhwESNKlBPRvNSEmRkk41XX2IuP23Dw37h4GkkKlrslwheEeNs4/gxcPIqJwYAbafVyERZmoBMgyD8I8woAERbj4x4c3ImFN40xTUCUIE90zwZOdUuQve3kbuCbMkktKDveRvN2O1x3GUydAA88DHtLj3PATpCXDd+9Cv7yFAQScCOecSpcdi58uATeNTpUZ2fCLddAfg787CHwxK1m554mpLf4a3k+uD+cOV1UecvXwqoNQpijhsrrXj988ClUdS0KcfaCPjNh13vHVvxzZcOMn8O2N6FqKzS253p2mEV7pcU9ByGyqCZuQrel9TZmw2ryhFviSq2zIbqHiCYPZ5v7xmZK3viYPxjuGge/WgmfHxTX51l94c/Tu3lwABYszKAjeb+uQ3glOK6G6F4htqPnQhHvIRZ56GGIHgB2Q+hT0CNAWNyjrRBs3YLPeirYL4DA28a2SZyXxFSdm0EZDEo6cOTEqGX7DYLTz4Vf3Aq7t0JOAfztJSGgb7/ofF9VhQ9fg0VvioX36Bswdgp89ZG8l1cIv/8p7NzcOi1n3XLY8K3URM3rDY++DoV9hfiK+ouV+PI/oPoImM0tIpamBnk9K0esv+7CzAQUMro/gAEdDa2DFUiMMB/zB1xkcjX/oJEjmDDjJptVvMoKXqD5F+zNGM7llzjJIJ1CLNi5lD/hoRov1bzHL6ni+AKpCpmGxZfM3aoTZQVe5mNiGGpGAfpI0OwVaOt3gS0AIyzoB/0E9v8VZXwqOIA1UchUYKgZSmNoZfthglGRY12EcOhlInyOiaGoZKDjQ+MAMboInlktchGlp4ll4/HKBXLlhbBjL6zbLCTRxhKrC8gDECsqPVWsooYm2dZhF/IMhKDJuMvNJkAx+leZoa5Bxn7pbZg0RiypZphMMqaqQH2TNApWVRm3+XSHwvKZZrNsC1DfKORjtQipRqNGgeCQHOfAfpCWInlU9Q0tcRObTV73+lrIfslS2T6nV8txVdXCi2/BH+5vLSu3WeV8xRNqv95wuAoiEbjrFvjpb+X8/ORWeH8x9O8DN18FDz0Guo7FDSkFhvFbA/5aQAezA4qmwbBL4NAa0CLynhYBawpkDpLQ4+G10FRu7NcWg9PFqjtiVFaamCNxsYMeEaysroQ5RVJSp9n9ODhdBDFPVp7YNIK6oMQKJ+WC3dSiGh2XnRzxKcg+FV54d59YtaoCA9LA3H3Pk0I2JsZ0el8rqUAYtCpQUpAQWRRsp4tbU00F7TDESsGUa8T+pFZFuym1bRczSqqQpmIBNYekzn9iy3A3KOPEPx5rALpw9yeCwn7SeaLZ1VhbCZWHYODwrokv4IfSvTIXNdRCZYVYfs04VAYH9rUIhJqRlQ2nnwO9i6V0XGa2KDUBVn4JU2bBH58T1+miN+HgCfieLTAb8b0EffwdQMtW+WrMK9hXqAT9bfzCBRYYZqd+WQUvhb/HAKaSzzBiRChjIwdY31K9JcdM02g/qze/jVqrQ7EN9rXkZcWI4KMWrAoUWeW9eKjAOKchje44UdTEQNSjLVuSg04DUb6FPmaYboWtUZitQLYN1kRgnkJsyWaYY4PVUSACM6zgVKA0AqeqkKHKKTdZYFkEnUNEk4nxmc1w+03yf1EB7NoLz70BF80TK2zfAZg1BZ59DQ51kODcpwDu+K4Qkj8AjzwrRPfDG6BXphDSky/Cmk1wxnSYMAoy0uRGfuQZIYZQ5Fj34LxZMHuGjLtrHzz2PBTkwo3zhdD6FMBr78GHn8H5c2HGJHA6YdM2+PdbcMdNQuZupyT71jXA2wtljDtvEZJbvQFeeFusuNuul2114PHnoeSgWLzhNvayrovMvK2Pd/YMuOg78N4nsNBo1PrpUmGxnCyYf74QpWpMpjv3CrmPHAKAuwBO/62ku4JU//vifvk77rsw7CIpeHTm7yUbY/kfpNLfhFuh6FRIL4Zp9wlhrvgT1Le9v7Mc8KvJImYpdIv444MSISAdeHEHnFkkltKbu8XSu3kkbKmFxSdY+OCNwCu74IEpcO9EWHkExmbDxNyWYqwAOQ5Jlci0i3AmGIN5fcUC3VILjWHYUQcX9If5g2B7nahLzy0Wi7KbMDHkGGV6PNRUideFvhJPofO74HkAfE+IpRYrA+9fQKsD79+EDM0DIPwtoIH/VYMImxGBwAugxy1Ywl+LG1XNl7FiSczXiSWwDwEOgNZgsGzi43eIaESud9WwlBVF5pj2RF3HHI8q90PzfvEdLkD+bxsGsTvg7j9Il42Fb8i9OSauBGFlBTxwOwwfB+fOh98+Cfff3ELMxwuFLMyMO/74ni9GydhNcgFrUVkFhnWwKeCNwWQXrPMTjDWxreALtvEFVETAoUKuGSqNE35ZBo3ROtZtegt6ARlWUIx4Qp4F7AqURyDHDKnGPnZFyNWjQU1UJrYprk6JT2Kaqcf3nQH2xGBvDE6xiDbokCYXok+HxWE4zQoHY7A0AqdaYIYFqjSo08CiCBl2B6oiRPThEiEnEMto4WcwczK8+zF8u75DdyeKAtdeCqVl8PybcsH7/HIBPv2KWFvzz4c5M4X48rLFgrrnQbHWOhoXYNkqWLEWcrPhgZ/CKwug4jD85Z8wqBhuvQa2Gg1SP18GXy4X8r7/R/DeYhhYDM+9JgT40jNww+ViUerAv14X6/YP98GSZUKcvoB8h8vOgSsuECssGXz6tRxXblwHDYsZrp8vi4etu6Dc6Oe4fC089js5hp89BLpO7mix9j68SbIZbKkQqBOhyqbnpe5E/7nwyR1i6YV98t66p+DA13DWI/DlL8BT0aaITEwX0cpLO0XI8tPxYDHBO/vgkQ0tBLGzHm79HH44RsgoqsPqI/DEZom9OUwyTrmxIC1tkjp0mg5NYcnD8yfhVH/DINfLBop4Zn01/PJbIa3mFISRWXI8VlWUpyZF1KD+CDy4VvL6XtsteX7XDhWX7fZ6uH8FXDqwm3kuYGEKdFbswmx4CCNCTroPCEC0HKJtKkFqhyHwauvXom1TM2MQaZtP7oHg23QLibk6N4LuBHUOaG0Dwt3E/j0yBwwfJy7I4kEiWNm+qet9nYbYZMcmKOgD+UWwd3sX+7iheDD87RewcSWMmQypcXHZtAyjDusqOFwOD78s7tCKAy3k2tyH0Wz02NT1uPfMoJqlbVVze6l4mBjckrR+PPBrQnAAg+1QaIHSMAy0waJGeR+E6Ca6YIQdPmyESS5ojEGKCp80CaHtCkFIg3wLnJECGwPw/9i77zA5r/Ju/J+p23fVtZLVZau4V9wLtsE2BgM2vYUUEnoCTiMJhOR9KQkQAjGQUF6KsYlxMDYBjA3GBveGm2yrWL2XXW3fnfr8/jjPSqvVlllpJYsffK9rrt2ZOfOUmXPO3b73fS+u4uJGVvYF4TY1zQXxe1PSnFPPvCxf3EFraV8ywH7IxlbuQQr71jLr0B6xvBh4LRdnebZIZ8RZKbaW2FXmmHQQcstLYezLYgbtHQeRp9fTy9PLg8Aa+FqhEIRB1whB6XQquPO+euO+4yZNCK7SCY0snBs2/H4sW0HLKIzHZJJzTufMU8KknDQh1hzLwYX5hldx/Q9YuyG8f+FZnHI82djlmkoFl+Ou1r2PchSOu2MXm7cFAdTbF6y94xYxeWI4T30tz64corzXKCgU909ILpb4yS944pkgqI9fwsYtnH4C//bVcN4rL2HFC1pWlJTyXPTPLP8hG+7by87Md4XiR+U8fW37sjbzXcEqLBfD39zgmGuhzP95ZO9cvu6pcG+d+X2tK0IC+nvujotaRyFe1j+mt8RH7t/7/OZV/OCFcPyVu8Pn8iOTJJZMYFtv8EjqKfKVp/nu8uCW7CiE3/eODXuP86vNPLh1/wNF9ibet/QFgdmYDUuxsxCu6f4tB2j11Ug714h9WFvp/gLppeii63MxO/MIQWWCbxfJU4lWMl49ELdu4Mb/5F1/yTV/wMQp/OK2kDIwEsrl4MZcenKwyqY2h7jd03FFpXwuxOQGm6UdbcGF+scf5uWvDet+7cqwd8HFr+RlrwnjGppYvTw84JxLwntLTgyf++i/c//PufPWQe+l+YfPc/8vuPOH+54/5LGNc/5cOkF1Mgi52kGTcFY2CCqCq7IxyY2tvHtKEI4rcvymh45yMLPz8Rc2v4onerg31lo3xC6rBJZWh++1MbX/+YZA0nRpJx68lbuxHB4ESw+eKe79jb8fu2Ej7CzwYGHve/89ll48wyCK9g8wiML3MkppJKVyqEwxq5nHCFz6KLj95szkU9fxmsuYP0ApGoq1OfgrnDKRP3oT//cLQSCfsCS8XpXlD9/IU8/xULyYZkzjbdfwsc+G4yzpJ1jFsYDBsquxPsT3UnHMr7M7uFsffyZYlf3X2C/0hhKA/dc7uLbafrXWUsFFvH0X7Z3MjIM15SjED59bFazZmhpta7v85N0cfXns2nwdv/ir4Lo8aAysztI+ipJUKIf0hiGQLZadPIU5DWzsijy2I1Kb4czpZJMlD+8I1XoWNoViLLPreaY1kEUXNvEXJ3Hf1uDQebol5JpPkDe/hkxdKBRTo+SMWYH38nRLZE1HBYzDUhTYp8Pd8xiQNDNODRrllKvH5n48nKgsneFitBNtGIJpc4Aol/npzYElOf2owJ5cvzpYSyOhrZWPvz8wP+cdE46zevleJugTD7Fmxf4VVIoFvvzJwM7MZFm/KlhoXXFy789vY8Uy6htDA+B1LwRWKIEVun1Qk+e21uHfa99PWa+Sca5xyQU5pioItVNqeb6Xi+rD83U5jq0ObspTa4N1V5MMQm5bgbokb5zIlkKwGDtKYeIncVJN+NxJNTzfx5VN4fndneG1mdlw7LpUcHe2loJL5eQa5mRZWLV/DJBATHEQvRJHwkjMuRGMkGwycAU6KvXwRELl9+KgzaUcBavnja8Orsnb7hzaSiuXQ9zsfe9k6THBrXDDD9m+M7j83nQVJx4bYoUE195g9+bM6bz6MubO4o2vCnHBx58OJJDLL4qJKfkgZE85nssu5N5HAlnkgcd4flWwNq+8JAi0fiJOZ3e4nq74/jq7guu1VOLdbwtxwrUb2bCZW34a4n7NU4NVePvdwTJ99csC25Tw+VvvCN/HKy4O1/3Hb+L2e1i9LtzDWaeG43f3cPsvg9XbWB8TfRqCcM3HLpO/fm+wMB9/mp4eySw9O3nym6y5i9dez+RFewVfuRjquidThzZbIi2rRpP+9ZzXLRcXTb96AWc388A2plSHSMS7jwtyp7PAh2fwuSf5xzNY2caGLv72FP7mocCRmdsQXt9ZE3vZp/JHS/nZhuAtTSVCit/U6kDw/MipfPj+cOzDhZTjJTUfvhMeAiSMtE2kSZxO8qXYSbQxWH1RTIKr8l51/mNEkzfnFl3eyKCWOb9LSJqn0S+kDF8mv8fH9fqn0Q9WnwxCrBixuxSeJxL0lUOcryZJIQqCrzEV/u8rx5WPk8HCK0RBgBWikJ4yIRU+2xftdYdmEuH/plT4v7dMbxTey0XBRdqUClZnV5nu/bXHGh9X42PDWnwla3S4+ADbDI0dqQQvn8mxTfx4E+u7OXFi2JSe2c2CBiZkacmxqt8dlsCM6UGoDXbT1VRz3OKwQz39/PC5b4kEs2Ywb3YQQM+tDDvzkqOprwvCpVgM52hqDPlVrW17P19XG6zD/tZV7R3BFdkfD9y6k+7u4K6srQnn6v/Od7aEx1HNzJ/N5u309rJrdxCgrbuDC7Ml/ru7PRBeJjSGx3Mrg+CH5mnhfH19gXjSmwtu3Kq4jFY+z7pNgRQzMI63bUcg88ybHVytA8c2Tw2vF4usXLP3vqdMCqkOPb3hXH05Cy8LeXgdm0Kf54ZZ3P7+ELODaSfyii+x8QF6dvD0DXRvC+9NXsQV13HbO+k8yPoFx7rYm31OUmDG/drX3eHfZZN88Xy+vCxYawTuyafP4i8fCHyVz5/HN57jgyfy6d8E3sznzwv/b+7m387lumdYEX8Nl83moqP4+4f2Eh3rMxw/KaQOvuUYPnBvCO8dHiTU+hfV/nKEdf28dheLbDtcFzVmjGzxReglekbIq+iJcywOORICgyErIS2YJZFQziov5GD99gjSlGMlzRifg3WVw6MfHQP+z0X7Pt89UO2NaBnwvG+AvtM2SD3uHHCM1kHvDXzeMpJaXbun2PSRgigKQq63xO48F0znJVOCBUgQio/tYtOg7jnDsjV7+3isgqB0FIW41cb+HTeJrMSyrcISDPpnpJb2HvvN7e6ekAYwGNt2hsdAdHQF9+BgbN4WHvt8Pi5rt3XQ32079r43ePzg11cPobS07B5g/SaFe6yWeKHNvqzmetGmztDEVtE+ScL9cccB2PJY8NLUTGbHM+F55wBvy85lQRBOOz6QXwoDSM8dm7jrI8OkMYwR1RrNdKx0XIGp0fRwp4mgXA3sA9tPUC3F3ttyFPSaXCmE8CJ7uQLDoSO/V+gl8Z7Ygny2dW8U4nBhbOUHj1yMLPhKRE+hLsT4rI5DHQMTOMcFKQnTpSyWdsIeF1nCRAk18WVGIjl0KtulbK2i55U8q2ytyBCBvRcV6bgw8hRZrxQSzYZHKKQ8ecQxB4LQxHIc6xmOiASq4xp+J0s7bpQFkop/4wrK9YwJJZF2g+dDGdt7Q4H93blg3bXnebSDLT1BI3+ilV17DLdkXBlmmCRdnSorbp2UMEXKUmknSzlO0iwJEyRUx8fPCx23tytZpeRJRcvGpev84UMyrpwzR8rRMaFrnqTpEpritZy1dwMpiPSJdIq0KtuqZL2ytco2KNsWr+ugCPS2sOonw589KrP9qfAYjEIPWx/f//XxRK4ULL03Hs0dG2NveGtQpK6cuzfEtrFrCE99rJR1F4J7MxIqn0X2HZtIMKs+xAEzyVDg5dDvev3reqK0M6QsGWV8StIk5XEvjViMC9Qf/B1X3JaonCD1WpLnUH6S8m0qNLpGsgrqZZwl62ppF0iZI1BkE6NqFNEeC7BNySoFP5d3m5JlDlWl/aGRQFW84JulzJeyNLbyFko6StLUUe+nyjtkXDbO1xbp9Rl5N44+dMxI7RHsSbOlHCPlOCmLJc2NN7thiubGSGpW70bj/XuVrNbtXaJBLZtgY0+w8k6ZzK+2cflRTK4K7PBVnYO0dbPV+bqkqfsdJ3y3n5R38whXkpVygiqvl/FySQsl1Bttfoe5nVe2XdEj8n6g4C7RYS41VxkScarOS2RcLuMsSfNihSEzJssg2rPN50XalW1R8pyihxQ9qmTlEajg7kWEby3nirmcNyPE6p7YyZee4RVzQ/zuC3Hmww0rAz+mUObGVaGcZzni/y0P7s3jJ4Xa0c+1huYM/ShF/NezodPRth4+88ToXJyxIT1oXS/as65T5khUtK7nqHcz4yz4Sp7X7c/G3J1lKFSWx3cmiZmU/pNoM8lLUUsl54902d88zMq4QLX3S7tIQuOYTecwPh3/QFOknanau+T9rz5fVvLMEOc9WCQq2OwbBBft2O6QyDS0AAAgAElEQVQnqXncA8aR8jCb9liRkTApvsb50pbEVku/YJ8kWLTJMd13QpW0peNwfYORMtzUbs3xjVV7NenvrN5bLOKOzYO31Ky0YyXN3O84kUja2fL+x/4bcULKYtXeJ+uaeLOovC5iIlamUuZImi3rSkW/0edL8v6XCizkfpuqP0iQiI9bEkkNuOKDWSEJM1W5RtZbpJ1AXPbuwI+XsNe6qJY0XcrJst4k0q5kpaK75d2u5OnYqj+y0Fng+4O80tt7+eag3LV7B2Qg3Dfg/5Vt4dGPTd2DXO9CGt5zY6jvPTyye9Z1UNiPHaCwz5Q00YGt62ppx47HBQ5C0cEWAOlHZRbfOhJHxeedQPl2FdfsHCz4Eqap8WFV/ljC5HHzFSckJcxQ5U9kXKrXv8q53ni2m0mYos5XpJ0eb/a1xjopfhtR5c2q/VUs2BsdiGA/kjBwsx/oShqLHZGIhRtV9nVFVsm6Ro1/kLJ4TAJvuPOEvKlz1DtJ3i16/F/lEUrJpSVcaZq77HK+yVbocraJaqU8ps1LTFQtqUfJTbboGHO8vEbWVWpcK+XkOA5/aOZDuP9UvEGfJe1MVd6j5Ak535E7BB6D3xVU+UPVPihpWryux2ah/zajsnSGs0jMQVxzrXxr5ScYaJYmHa3O52RcIREzosYbCUlJ89X5rJSFen1i3DTDYKGcJDUeiei/RQgxm9Hidb97SJorocne/oD1avyFatfGMa3x+77CseplvU3Ksbp9WNF9hhLXRZGUhLNMNFeNvLLTTbBTzima9ojiksgkmTEJvqC4fkSVP5LQcNjnRHAST5BwkbJdcv77sJ7//09ImX+ILLMjH5Wpo60kppE4h2gMYYZItEfwJS1U779kvPKQCb1+JGKXZLUPqPH3gmX2e/we44uk6QPYurVqfESNj0iacMgEQkJSymnqfUPGJYaztJ7U4QrTPK/LNjmb9HpEm99o16OkS1HfGB2dSTPVuU6190keQHhifNEXu5l/W4g/v8eRhIosvvJDscArE40xEz/SKWGqOp+N43kH5/oZCxKqVXuvkrVy/sv4x/x+j99lJDRImafkWdXeo8afSxwGJSshEXtPrtPlnYoe2m/Mer2ut8kq3XqVlEUmyNiiT4uCpGDxtVZIQEiYoNanZb02dm2+uChZruDXh+joQb3o74Ny6Mg0wT/Vn85yaM/1ewxEZa7OCyg/gurg9iyP0j1hL8rIqfYXMq4cUehFyiJdItuVbRdpFelBJFD9J8UB1+moG4O2WavGXyt6SMkTlV74CNeZH+DaqhQZiVGCspGi8c9NDMzXg0cJuTEuyaTRYgb97MXxX+yH4phDISvlWBklNf7GcJ6FcJ99ynaJbIv/dgq/d1bSJAkzJM2M3YejK4dB+C1S67O6vHW/IgAlkScHhBmeP6iUkZQqfyLr9RUJvXC/ZZEOkV3x/bbFqTV5e5nQjfG9TxLSWuqEeN5ojO6yvB+JDJNfeQBISpvuGIudZ46TNZgqEmm3zTqPWel+u6yL96kDW1NJaU2azbTELMeb5mj1JkvLKsrr1mKHNTZ4ykbPxA2jD908jhQPYC+rZF2HVJXxvvZoHGO5laluDSSOJVEldMIdA7LeKO20IU8VxRnyRU8q+ImC++IcnjYM7HqbFLpsT5SyWMYVsq6KadMjC5SwQcxV7QO6vSc+7oGhbJdu7xujVp+M2X2Xjzgq7yY53zPek6VklOrdFSDnZkUVJGoPQMpCtT7BCPVJI9v1+IiyIZKlDwJBgRoirttfp2jkekUVIyEh63Wy3iphyn6bQaSobLW8OxT8XMkKkV0ivYLQ67+YrIQmKUfLeJms10pZMqqQSUhIO0uNv9Xtw+gdcfyBImWJau8ViDzDI4Q2dim4T8EdSp5QtiUWgP3ruf+LD4ntCTUSmiTNiFnSp0o7RcrREqbEYwZ/rzvk3Wa81kqTZpd6v3O81USzJAcI30ikrGiHNX7la37lG3J6lBT3JLCPhhqNjna2011jkfNNNltWjcFpLf3pHDk9tnjer33Dg27UZ5zqRA5C3ncVPTCmz6QsVeufjRQ+Ktusx98NmU50MAjzaHxyfitzdd5D6kqUKY2QQLo/kjJeBkNM3pKSx/X6goI74i8pnshVaaZNoLWTvgJKlDqVdSrbEHfh/qpqf67K24xmAYYN6pVy/kvRw2O5gUHoU3TPGD+TkPWqUUeFXMQxfbmHHok06TrlwgvKBnG003VUTaR7s6E2oLKTRQoj6u6RHgV3H5KSZclGlCnH6yRRx8R3UuoIFYg6/5doHBTIlJPD8QdtYJFt+nxVznfi+xvJSiiK9Cjaqug+Od9Q5V2q/dmozOeElKy3xHmstww7LpmgOh2aiQ9sk5dKhuXWmx9OjCRlvSkm8oyk5ecV3K7XZxU9bnQhXBIS2HuF5PW18Sb8HQkNkuZIO13GpdLOlDSbWNAU/FrJ86McvzJMNtfbfNFJrpAagnsQ2LsZzRa5xifMdKxH/Y+ivKqKFOCEC73La3xM9ShkoP50jmr15jvdLMdpttgtPiY31IafSJGpJ1VLtonuTRTjcdmJ1DbTt4u+naRqwprN1FPoJFOv1L1SKVq+/3FHQPDEjbyu6VLwC7+9JctiJJYI6RzFkNZQKcFluB850ivner0+EVemGIC6av78Ss44hi/fTksn0ydw+8C2DSUly3W7VsmquB5k0yjXMkXW6xQ96ncy1te0lO4NFEdooTMY9XNZ8Aae/lf79HmRYO6rKeXoHn6zHXckSE8n2URhk9DjK0G6mdREirso7SRRQ+MbKG2n5xFKu0hWU9xOehb51r2l91JTSU2muJnyASjW+yt0kZJlelyr4G5jd19Hytbr9U9KnlTrM6MKnYQG1T6k4F7DJblftpTmRn70DC0DpsDUet56Btc/wo4h7j9hsqwrRglTFOV8O9byD7ZVQiDElSxTskzODZJmSTtH1lXSTpN3k/EgtdRo8jqfcLIrJaVE8Z1s8JS1HtVuuyp1ZlhsvjNMNNM53m6SWZIVh1oi6zyuHM+DspKu2KW53SqtNsrricugLbXASzSaFivrtV7qT23wpPt9Z4gbaOaMfwkCL1UVmhD+5h+pnclxHyDfSc00nv9SWMszL6V6Kj2bqZ7G05+mbXwUiEODrLBXD/QUjA8qE3zTwjVEHQ46DBXp0+c/4jSDITLgF82gUOKm+8jG/cWOmz1I8PWjR58vS5ioxl9LjOB6SCDjUglTROPsWjuykWTCEo7/MJt/Hib67mXUz2byKaGk/Y4HQsHAxgVkJwWtMSoHzbB+HrMuo5Rn+/2UesMiqp5K6zMkM2EBNswLCy9TT/uqSnKsx4yaM2h8HeUOZGj5LNkFTPijIAhLLbR/l5qX0Hh1eC09i/YbSNaRPZpEhvpL6Pk1mXlM/COKO0nW0/L5IDgPFMEttlK3d4fO8Qe1WAvybhEpqPcVNA8r/ILL8wxZr5bz9f3eP2YqV5/EPavC0jp/ITObuG8Nm9vo6AtWXybFBQuZVBfG7uwKLuukBSPec8lTev2fcRB6Q6GgbK28tfL+R9IcZQdZZRokvMTrnO7qPUKvW6sf+YQHfFeXXbHrkbQqMy3xSh9xuqsdF3uxKsUaj3jK7SY6yhNus9yv7LRWn07lAZ6AjBrznOr1PmWR8/YIv/O902/cpnew+z6ZpnoKj/89+Q7OuY6a6cx/Pb072PgT5ryKedfQ8QLty+lYRbGHzOawto8owTcgFpGopv5Vwart+km86MevVmZlMb60EBrLOyiuRKQk53t6fXJooQfdORpqmNxATZa5U9nWNvRY0KfPdTIukHb+CJpxQtICKYsVf5cEXyIRFkftDLINpGP3TPXUYMVNOJbGY2h5grlXBXdI14Ywfs33aFhAuj6Uvq+ewpZfsvS97HiQmRcHQTchrr4y9Qy2P8iUM3jqZ+N7H8lgxRU2klvOpHeRXRQEWiJF9y/IrQjuy55f0Xs+PffRfQ8ioj46bg3W4aT3BEuv4Sq6fk7Xz5j859SeT+dBGLCRTj0+Pg5Crx9lBT/R67NxvHSkAHtWlXfEwnLf2Mrmdlbt5KF15IvUZpk9kXMX8P0B+mQ6SXWGRdPo7ONnz4vLrI3URzKS90Nlm8Z0ZwkJDep06t4jYEZHbsTE/bGgwRQXepdM/J2W5P3UZ/zCdUqDmK79VuB3fVC1eid6xZhSOfJ6fM+1inJ6DL+XFfRa5X7fc60P+IHJZktIOMpxpphn41Bx9r6d9LUIfv1CUEQb5pHMBqGXzLL7WVKZIBxTVeTbSVWHhXOkIJGl9hJ6fkntheSeC6+lJgYhWHMK2YXkl9N78OurotyC6EHKT2OrAxa4QTN8Wq//OzTxoB+rt/HIKk5ZwKUnUVPFjx8b5djb9fmq0YgrCfXSTjmAq/8tRlRi5yN0rmXTHex8COUw+dO1pLLUzwnaY/sKWp4M4wsdIS7Q8QLrb2XdD5lyOhOPCxZk3awgJCeHGJft99G5jm33hOMmxjltJRVckslq0lOCECtupvchOn5I05uZ9P6YgBWXY4kGlGVpeBUT3h7WkWRY86mGYCUqU2qN3ztARCIFP5b3I+PrlinK+aaCe0YUEMHqO0Xa2fu911ugOx8su+ZGTpnN7p5g4VVnqM/SWM3RU1nSTHtveA9xnuJIG2Re0ZPGes9NGlzrnepfpBzbhc40y/ESEiKR9Z5yr2/tJ/QGosMOd/h3vQdQK7LD9hGF3kBs8KTl7t7ze9doNMmsoQdHg1MgIjrWsOuxEKJ46pNh/Q5uFHykISqEBV59Bpn5RMWgraamU30KNafT9VNqzg7a7kFi5N0piSnh/FrDtSSOOdBT9enzBWVrhn47gaMms2A6K7fwhR/zmVv5yWMV/WgFdyuNog2G7JyTVJq3DzU1NI1ck/W3D5lGjv9Q0BRbB9Q0LReDoIyK8Xee2NsvJcEeN0TPFrb9mpX/jxXfCIuvXIo/e4jipwX6HqfcS+/D9D1JaXdYK4VNdNxM1dK9a6LcQ3Y+6aOQpuq4oEQWNpCsCrfc9wy155JdTNUJ5J498MuLtOnzNeNZIm/vsXfr8yVGZffVyXq1wY6cKOKR9UH47ejkmc1s7eCJjcyeEATd7Ils72TVDta08FzMSxidwVyKWaqVoU6N0x1niflmmiopKSvjBMc4zbHqhulikpDQbIpMzPJsNkWNKjNNc5LFZmt2okUyFTixEhKWuEh2z71FnvJjHRV4gtZ63JZxItYMh5KCDZ7WL9CS0mqHKgxdLoS1GJXDo2dLiLuvvYmmJZzxaU77BJNODIpuvp18W1Bqc7v37dv0oiMKi7r+1eSWBXdOanpgp0XF8MjMi/8/+BStkWdJksRUEqfHCnxVTCgYI0LQ+HF5Px7hSlJcdUaI5y2ZxYZd9PQxbxrX/ZSfjZyDF9mu6GGpWIsbDinzBJfRvhtUdXVoZD15Crt3094Wnl94cXjtjp/Q3k7hMHY6HjeUi2GyH3VZ0AS71gWTJ9vIhOPCzhiVw7hk/H9UCBOsdibzrmbiCex8lN3PUHp1WEzF3vC8XLDH1RLFfw8B2r4bYnxNb6OwnvyaIPgarkIixPJKsVLdeRuNb6ThyvB6+800vjbE8tq+HYRm5200XkPT60PMr/cA29aE+f2oopE9EweDonsVPSrt4hFjfRnnSWrez/X46ADi7E8GCfhVA+KaP3pm/zOPjKykKaOMCUhJ+jNv0KReSVmzKZIS3uQKcx2loOBSZ/s331YYdN6sjD/3dl/yPS12+5A/cLOf+WPX6NarTo2Ssm/5oceMrMFk1JjjpD3fY16vFzykEqu1V7uNnrbwEPek69UmEukvLp4aaqvu3cZTn6LUh4inPh3Ia1GJx/4usDpLeXq3DnBrJsIaTSTHRYCMKwor6botCMBEOijd5T4KL5B/Ibg6O28hOvi0nZEFX5HoeaLtQieGDBXO8UEoybtZZIQukIUSX72T4+dw8Qn8150US5y5iIWVdC0oKXpUlT80kmsm5FvVigYJvldfwxln0trCxEl87tPMmMmb3k42w/Rmbv4eGzcMc+AjGVGR578cYm/JdPD1L/t8cFlu/DG5tqANtsfU5mJvYIrld/Po31J3FFvuCiSYUl9woUw5PSygXCvrbgmaZF8rfdt44XqikVm2B4JyG21ft0+MO7dsgKU2YN/Kr2bXp/a+nn+eXcv3H9f2beOQ11dWcLtDwuiJEWmXd6u0C420bJPmSjlhzDG34VAWl2wadk2lZVwQp1KMLCQnaHSco33E51Wrssg8jeq90kV+7gF5Ra91iclus20QUSaBRnVScVH4RnUyMrr0uN29TnecrXaZXkFPyxqNJgzouNGjXauNo36OwLndPk5xxqS0tKy0KilpKWlJaSkZDaYZtfB3VA4KbT8G/l/sCmGKvW+OyzWPCclUsBoaJwTLYtVy8jlqajl6Mbk+1qyiWKRpIguODkzU1WX62ql5jrkL6JnBmpX0rGf6DI56CZs3sn0r6QzzFgS33KYN4bUKUBmrcybJN4f/yw8RVTZH9iCyXcEvRh9YKpMrMncaJ8wJRJdT5tNTWdJ5cKP2CT39hkaojFG73z43ZSqrV/G1r/DBaznrHG67hV/+HBE3fJvSEaMgJSVl4nyxoSokDLGTd28Kj360L6d9uYSUhLRISZQfQIrIx31Pcq20DUqC79nMhs32Q//CK3QR57cdEgz2pg4ntAa/Xum4MSLSPuZE4ANBwa9FdkmM2L6qRtrZsSA+eJStM9KaClbmVdKuHzVHNh0Lz7yChIScvIy0tJRO3dp1+aLvah/WpRsSvsPsT+85VlFRr5yyckVWWJV6NQMIO3069mdMjoAOO0TKRiueMRhJaRMdZY6TzHOqZos0alajQUaVlIyUjKS0mhe9Fuo4oKmJj3+GZU8FAfjCCm75Hn/w7mDNTZzEM09w5495z4dp2Rk22bbY5fbBv2HdmuCR2rWD+gb++P2sfYFXv4FvfoWuTs6/hGSSN/4Bn/oHdo+eOF+Z4DuR8n1EWx2QUlu0TMm6yga/sIWf/YZrzg6m1sot3LJ/LcKhUI6rYiRGFHxDl5+JymzfHpSPHduZODl83+VS+HtkCL2kCc4ywxvVWiBS1GOt7X5gt/tBkzM0e4N1/k1O0H4SUmb7U7DRV0VKsqaY5rUmOVfGVCWdWv3aFt9TNC7Nvn5nULax8vldCZJYmiGT4Nn8HmW9bK2SlSP2bQwkl1OFOX7wuW4lq5Rtk7JwhMudpda/6PbeESsFtevUrsulzpaUNNkE7To9Y5U6tVbHVlduCOukoKRbj3OcrEO3mabp11jGqrdkVEkPqEJT0Kc4hnJYOd2iEa3gfZGQMNOxLvQnTvIKk82RVvXbL9hGQzIZfpybv0NtPR/4G554lDPP5bbvUypy0cu5+w6qqunr5a6fsXVTEHJV1XS0ce8v2bWTy17F07/h5uu5+i2cfzH//W0euZ/mmeG4k6ZUJPhGZnlkSV5N8nhSryF1NYnFY7v3EP94RMVB/2I5xPP+9nqu/WZIYm+tTNoG9+Vo1mGwcAYjmeK4E5g0maMX7XVp5nNMmEh9PakXmf3b4EQn+LoqzVrcpd3jasxRZ8meMbUWmunNMgZSFJMme5nJLtX/k1ebq9nV8lq0uFPBbsf4uHk+aCzkn98jdHyPKmHsVSWoqWCzOybD9VN5fd0+3q5Il6InR6X/Jy2QcBAU1QEo26LooQoYpeerd4OsN6B+yHF98q5zo2kmKyn7d9/Rrst1blBQcK5TY5bn/ucqKfmqm03SpF6tz/qm1Ta51V1WWe+XHvawpz1t5aj3lBjUQ7OsPIaUipCEXun4pJTTvc4H3eLlPqjZImlVIpEebbZ7wWoPecYdHvdDD7rRr3zdCveO6ZqOWBTyFIphI00mqY0JRb29wc15wzfo6eZLnyGX49qPsvQEOjv44qepq+evPs7c+VTXhLHQ00VtHZdczlWvJ58PVkuiMmViZIuvQPnXRE+gGanKq7YMPEjJk2P9UFxXaaw/fNHoBWQThtrYSyWam/mLv6KtjYeDAeXhh/iz9/H+D3H9N9k8PqGTA0K9pVLqrPYJnZ4GyXgRjRWdnvKkNyrqRCSpSlqDyS611ueUDmG86v9PCEnrq1TUDPXNdcFI+MYI320G76xneYEvdgw6bKSkn4U7vBaWNFXSdCWVxTtGRl7Of8dl9xqHHdXPmK73dQV36fM1Rfftl6+7xkZf9r19Xttpt+/40ahX8oINrnPjPq/tiHkDg2OCI6GsqDRgnwjuxcq12tQQ9UOHw1Iv9VafN8FMCQllJRs97WE3WeFerTbq1aEoFwvUIIQv8T6LnGssDX4TcZpOub8MbPxaFA3isiSCol8e/4Io+2P6DM4+n4Ymtm1m3Wq2bqZcZucOujoCi3D23GDNLVzEzFlh3PSZPPogxyxl6vTw/mWvYuM6TjuLX97BkuOCGzTXR8Pw83MwRhZ8EXaFPnzJ85ELdTvLY9j8I13j6waq4IwjY/iJ9PM7uOOnQTnpd22uX8vH/z6QTnsPTQ3gitHlOSU9FvmkTb5utwfkD7CCe6SopFeVZhkTJVUp6ZNSO6RF/NuEVCqsq8OTuhQpWTv6sOoEr6jlqZw9JSEj+/NBqhLc3ccLBXbtr8SVKopj10uaeWAK5xAouEfeHbJeN0rptAQaZLxaxsUKHpR3g7y7hLqNR0apwJwehQEpGFVq46LRlaFa/Ygl3PaOa3C5Dw8QemWPucVN/tou6420boctw5Fi8TVsuo+uLTSfFsjZu57l+HdQM5mODSy7ngWXM3EhxT6qmsJntjzCcW+haR5ta3nuRgoHwNSvGK0tTJkWSCjf/XpwQ/7Xv/PSy5i/kPvuDuNmzwsC7/lnePDXwXI7enFwXT7wK5Y9ESy62lrOOj+89tgDgfRyxasDCearX2RnZR07KtvhMpQfFup1jjHfNNJ+iEoZjS/Wrg6/Ue8QHtl8riJ9/pCj0zOW+RNzvM8Sn1PQaosbbPLtMcfl6iw137XqLVWWU5ZXa6GiDiF/L03tXCTpWUf1zFDtoWft3vcKHfRtpeaoUCGiZ31gkL6IaKrnT6/hxtvZfFgK9BSUR2qPU4W313NRDZdUc1yGM+L40uN5PtUWhF9tgrfWc0UNU1OUIp7KB6tv9d7vNLJdpGvEODYZyQGsxYNHl17/Iu2UuJLLyFZIIuZgZrxcxkWqrZD3YwW3KVrmUOQ6jgV9OnVpMdV8hJqd9SZrGVw3eBhMcFRFgq85rvHZ/3212OCHPm5XBYZAlTpDib+oRPUE5l7Mc9/jmNew9k7yXay8Nbx/7j+w5SHmXMi6uzj2TTz338y5KAjG2qk8/iVOeEcQjit+UNFtHxh6ukM8r3OA5b9+Dd/6yr7jfnTz/p+9+fr9X/vVL8KjH9u38q3/HPNlVdad4W6hF9/ZlMfWnUbow3Xku83uuN2hN/sPGmW73a/d4+os1ux15vtLWdOs8tHYzbv/TSSkpNQoxRtOSoMl/kXGFCv8jW4rlfU52sdMclH40OTzqD+GzufJbaVxKU0n0/ogqTrqFtL6EJkGplwU8oeqj6LlUDUH3RepJDXVIXyQi7WSZJIFR7F2M9tGyJwZXxREIykdETaV+FUf51bxTJ7b4o1/S2mvEZTGogwP5YKbsznFhxqZmOJdO/eErkNrlk5MH+GakiMSYA4EJU/o8ffqfAHTK3L1hTFV0k6Ucrxqf6roEXm3DujKcfhp9n067bTGPKdJSKjRaIYl1lfQrzMp7SjHVnSeqeapGVA8f4MnbB/c5WTIc6RMc/Sw72/4Fae+ly0PUzOJHU9RPZEFl5Gppa6ZdDWFHtrXBMuwfS0zz2DK8Wx+gK7NbH2UWecdQsHX3cVtN9F38CSr8UZlrM5FIZ+vfABfUKRTNJYeeMkEVZnQJ2VCXajbuaU1pDocQhyqgiPji5DAVtan01O6PKvKdJNcKKlWSaeS3rgWxl5yQ5VmNebrihN7Myaqd6zNvmO3e+Mj16gxb++paucGwdaxLFh0tQuCcMtOpvXhUMy6YRG5HXSvoXczE041UqrmeCGb4Z1XccycMFW+egvLXuCsE3jDy7nzwcPHwg2NiUfwFeXxs14mJXlPA0/m+d4Q4zsiPtK61/WZwOQkb66nKcmOcny+nlEVyUD6n2zcGg+CcpyrV1brUxVZfvteU1LCFBlXyHiZsk2Kfi3nVkUPiA5x09WBKCl4wYNOd02sFGYc7+Ue9QOlUXw7Taab4+SK7j2rJu6wHtClVbkCQd+k2UIvGfYcbWso5lj6JnY+E9JyT35XsPqe+TaTFsUDo7h6X7Tnqb7WYPVBzRT6DiWBu6+P++85hCc4cFQm+JpRjvP3OhhpnQ9GYFqOwf21YDoXHMcPHuSjr6epjht/zS/3KynxO4fpXqPGXJ2eUtStxlxNXqLdw8oxdb3bcgXt5vmQpCqh2uKb9hGERR36bDbJhSY6T1nOVFdqcELs6sTux5h2CfWL6Xg6uDKjQujpU90c6n5FJTqeo/nKYAHu+tVh+R4KRX50T1jI11zCy88Ogu+x5zj+aGaPr7EzCooOprnxPqhLsCTLnDQNiTilAemBG2B+v+ILQyG06RpPwQdFeT9Qsk6tj8W9NsdGyw9jM1LmS5on6/VKlsu7Td6tSpY7HIGF592jww4TzJCQcIKXm+dUq42UOpVwkitNHyG1YyB6dSgr7iHONJoqJas4wnxJSjvH2zQbnj5fLrDxHk7+M+75m/Ba5yZmns2x1aEOdSlPXxvlfKhPUcqTa2f1T4O12HAUDbN4/LqKbmV8cHRNiHVvyNExDpppTZLzmni+m01jmzMVCb6oJSa39IRYXzRUh6BhkTOmoPbkxsAkOXsx29u58V4uOPb3gk+gUUz3WrP8IYIOvtv91vrXOJGdbqut9A/m+YClvqiky04/td4XY+EXKWrzgn92jH9whq/r0KrNQx0SZz8AACAASURBVFb6qKmuECnS+WyI50lQ6qH3q4jicmQJulaF16MiG74dou6lwxO7qa/lmkuZ0MD8mayLyYv5QqhyV1c5T2EcUHLQvbpgcYZ/mci0FKsKtJSZktovzBPc2aO7jhJqjGcbl32v4DFd/kDWm1V7b9wtfuy5PkEI1ko5RY2TVftTeXfK7UmGP3TzaYvnLXOnc71DQkKTGa72z77tPXZYPcS1Ji12viv8pVSFndd3WadHu0bTwFynmu0Ea4cpbZdR7SxvdrkPD9kUdyDW3cX2J+mOw8srf8jOZYHRuez6QFhpWxuswMe/FP4+9bVg4T34qSD4OrfQUxkX5MCQTTCnir6I7XmW1DCriltbyEfh/9YCrYPWz/QMdSl2F+koMjvOu96cY0KalgKTM7QVaUqxsCYIvgkppmbZlKN35HlfmeB7Ml4+JcbYsDdO9ByD1rm5hbddwBlH8/n/pbYquD1/Dzv8SKt7pDVKSCvpUdASBFWMhLJtvq/FnTImKOlV1CLak79YkpTQ4i6R5yyy1BOeV9AiIdLidiVdwV1W6lXu/+0GC7XiAJdNeZwsngpx4jEcv5C/u47XXcr02HWTTITYXyoZ4n3lw+a+HsP8Hso4SuBdDSzI8OYdrCgEWfqBRt47uCVQeZ/fe3hkhjnZ+CCyW85XFPxMlXeo8pY4f/BABWBowFPlnbJeo+CXcr6q4F6jd3MfO4pyfu4/LHaBKeZJSDjWxd7jRnf7qjUe1qtDSsYEMxzv5c7zDpPMsdtmDabsaWk0HLZbbY1HnRS3MZroKG/1737kE9Z7Qk63hETchHaJs73Faa5Wo8Eua00wc9hzlHIhdrfneT4wOwePIbg3oS/eRnt2hMchx2kNnNPIo51BWBUjGtMcW8vUTBAq0zLcsCMIMYJ3410zWN7DzCpu3MEp9Rxfy09beUkD393B66bwre20l8Lyq0vy1ulsy3N2I9/dPqLOV5HgS74sHpkh0UD5jrHc/RhdLZtb+OxtYfdauyP05dt05LNCDw8iRe2KI5RXOs/xVttimonyCqZrdozTPex527W6wmV65PzUw041Q6O8h2x1lXNM1iin4McedKGTNJvkDo/acIT1L9y8I+Qhvff1NDWwfC2ZdHB7nntycBi8/UpuupO+wyuTh0cpIhcFiy4lGIr9nsik8PquEhuKQehNSvLS6mEScitZU4ejCEGkbI1e/yznBlXeKOsNUhYjc0CVSRKxiMi6WsYlCn6izxcU/cZBNQMdAus94Vb/5M0+p84kSSnznWGOk3XbLa97T2eEavVI2GaFW3zM633KtFFcnjldfu4L5jtNY0wIOto53usmrTbo0SYhpd4kE8xQpR6Rle53m//j7f7DjBFcnkc8XuhlXjULqoN7c34NPSVmZJme5ZvbuHoKk9N7BR9BQN7Tzjums7SWKZnw00/MUJMKFY0aB4muxnRYT3e08p6ZZJP0DS/5RhZ8KRJLScwl2hCeH/L1FGH9gCz5Yon6kTWr32Mv+uSdaalpJrjdI+aYpkrG2Y61U5u1tnrUCr1yVtjkSmeCo830M4+62ClmmCwlaZoJjjf/iBN867bwd18MLs9dbYHIUihy+/38PC4XWSrtZXseWgxdEGE/dEXc1RdSG9IJOsusKfD/usKi/kUvn5zIv05iY5GTsiEekhtKyFWyCA8nW6usbJVen5TzbRmXq/ImKadJHGDNySAAJ8h6i7QL5XxFn6+Oa2pUpOxBN4pEXu2jplkgISktq2kQa7akaJ3H3eSvrfeEi71nVMEHz7nbTf7a1f7ZZHNjFmmDoxw36FoiBX2e9lM3+3u7rLPZs5otGubIvwWYnA5CqjYV5nx1klyZnYXg+rxmSpj7Wwct1KZ0eK+1QHcpxPJ6y+wuhMdrp4TpPTkdBGMxYlVvEJ5vmsby3nCeETCy4AvpOKJVwlorEK04iC9iNEyspzcX2JzJeHEfPydYfeuOrM33xUZS+N0LEW0DFOHnbXCls2y0Q0HRGZZYY4sGtTr1OMoUO7RZa5upmkzSaJIGPXJadeqTN98Mx5ilLXbFHFakskyaT1W/iy9i1+oQqR+AlvbwGIj2FyVrJqmimo0l/Fs764uckqU+EeJ4/XLt5m56ylxYE6y/m7tDnt/51XQMXMTJCgsMDFW8vEJMrmN6I9vaaR1LnK2sbKOcr8n7vrQzZF0j4xJJcx2IFRha8hylxj9KO1OPv1Mape1Q5UgqKXrAd633pHO9zbEuMcksGTWI5HTbaa0n/a8H3ajFBglJT/uZcmyB7p+isJdUVFb0gBts9pzzvNMSF2gyQ0a1hEAX6rbbFs951A884Ud7Gtb+xm1qNIhE2salCs9hxuo+dhTCvO4r8/WtgWKai4IHpCEVBFp+0DzdUeB/W9hVCBvcC71BuOUjnukOgjAfH+OGOEjZUeKmndQm6SqNOvVHb0v0wGEiGaeTvOp0Vmzmfa+gM/brT2vijtHza37XcHo9/zafb2znWzv2/kZdet3iXq067dTmdo9IS9lhty1anOJoE9RLScpIedILmtS5yxPadLnHk3br0qlbUenwW3tLLmfx5bTFLUCiEn0d+wm+IwdpKiQ7aC3ztc69obeBC6s34n96+EHPvu+t3VeaB6E3ugckNIc9QKuvuZF3X8Bj6/l2ZQXi9z9/u4JfKLhH0hwZF8l6jbSXSJhaUQL4XiQkZGW8Ur2ZurxXyaMHdF0TnazDCpGyWV6j21q7PaHkKP/jY2p9xmTzzfFKJb02uFOL1Xq021sUu+wOn/dz/wH7pShMcZZu6/Xasmf8Oo/b4En1pmgyXbUGoUNFty4tOuzcp5oMPOgGj7hZIBT9FjYCLUT7Elc6B7mq24dwXZcibm8Nwq8YL4LBDND8gOctg+LdQx1zCBw5tamKZf77Ppon8sOH9jaePW42c6a+uNcmLL0jpWhsXZL3zmB9jv9t3V8xeXpAl/vH7GuiPzigev7D9mcqvRAv1vvHTaseI/o66N7J9mfj1hjl8NoRioRMzKAcA0aaRqNOsazRu6KLa2RWMF+bajhzXmAGPbSWtl6e3Rr+rxsg0GdN5JRZoYbuI+vYWal5XVS2Rs4aOf8tZamsV8m6SspSY0mHCPVAT1Pni7r8gfIoBalrzNTgaGVFfXYo6lRthg4rlOX02KTaNAlpk50uUtRtg81WiiyXkLDFc6pNM90pOqyS12qSU5XltXpiD5saMiaY6CSTnaGgQ0ajas3aPC2vVVlJh+06Rqr0M0ArKispj0LsCbzYjB6FI2R3OkhEArHlEOPIKsOfLwYiy48fC0zO3jzPbuSeZS/qZaVlvNE7LXH8i3od/ahP8UAHf7WOXYehQlhimP8PCeqmUjeF+mk0TA+PdNXon3vRkJEYoXjzeCOhRmKY7gf9CH0aW4wq+DIp3nchx0xj8XQ+8NLgeRkKsydQm+W4Gbz/oiAox4weJY/r9U86vEKXP1LwU+W423glCJ0gXqLWRw3XBaIfE5xosjNNc77pXqqk10QnScfl3gayY9PqlOXNcpWpztXf53Kq8yz0x5ocZ4G3a7TINBfEObL7YoaXy2iQUqvGTPO9wySnmu21Fd1bjfmW+oLF/lW2wso7R2n0SS8zcazK1xGCxglcdDmnnc2CxWSrmH8MmWwgsM1fFGpYV1XzkvO5+Eqmxl/NhEnMns95l3L2S0M50EpxZAm+fhwzkytP41VncOmJ1L+4P2pRwWornOwMqQOga48nZma5ejKn1PEXM7ikKaTLjAVLavjAjBAj7Ed1kj+ZzjlxaO3yCVzQyPua+eRcFtfw2kl8YT6XTTiEE6drR+BmJ+JeXke8GpuOq6QcHiQ0SRic4jAYZWXbRj/Y9AbOXkBnH505TpvDhGGsyVU72dZBSzezJwahecCIRLbJ+55Ob9LlDfK+X7EATEjKeo2sV4xylrJuG3TbILgL+5T0ScSpHknp2HWc0GeH3Z5UVpTRpM92OS2yJkmp1mm17e7RZa1dHjTVObIDypERhGePTXJapFQjoc0yLRW4ZdMaHe0fTHWFGV5nvg9JViDMTjTdOWbLSsVJIUMjKRH3ed9/5fZ/Jhv3tw/Xkxxy7HgileLt72Hx8Rx7Mn/7KSZP5Z3vD+3hqqv5ww8wYTJXvYlTzw7v/9lfUtfAyS/h2n9iyvRQuzo5hss9clyd/Zg7jQ+9Klh/uSKT68PfDWPuhzRuWGiRl7pckwlSUr7vOwovQtnqOVV8ZWFwdT7cGRjBX1nIZzaHWF+lEZ1ja7l2Jne27XWR1yaDkLtpFw92ctUkTqrj8S7ObwoCcV0ujPvUXJ7tGXOxhMqwcwUr7wpWXikf3J3FIyUnYSikJB112M6WMM3IBaoJhbO3jDJGyPuIInry9Bb4wt1BCA5GXRV/dzlPbqJrvH+LLgU/V3C/jJeqca2082LhNBLqVPkTBXeIhknv6bN1T9H1lBpNjpdWY7LTtXhEg8XS6tVo1mG5srxOy7VZZpoLUbbVnfps1WCRdm0yGtWao9t6xUFuyJ3uM9X5Snq0eUYkUme2nlGKXyekzfZutRZ62h/EQvAfzfQWm3zTUCv7ZM1eYZHLHWOBST7ncr0Kisr+06OejBWflISzzfZmJ5prghY9bvW8n1opp2S+id7gOCu1eIsTPWaLn1rhfc5UK+Oz7vf0iK7ZA0ddQ7DuPv2RkHN72jkheydbFbdYSpDNUlMbrMIVy6hvDJ+ZMo1UmnUvcNuNY+/EcuQJvqmNPL46dF7PpNjaFqq4vIjYZYfv+7a0jGt9zL3usqGSVjTjiAT+aBpT0rxxRRBC2QQfm80HZ3L77vEXRL1l/n4Db53KR2fzF2sDmep/FjMre4gEX76byfOZdRr3fDZU3a2qp+tQlpg4cATW4QKHpkrK/kiZbzRyS6RbuRIW4PYOVmwPBU83toXdp1hm0TSOnkpNhqXN7OpmSj3rW4JL9KCsveHQo+Anih5T469Ue8+Isczg8jxDyomKcb3ZwWjzjME2UJunYquybK1vI1iGXdYissXPEFnnhvgTkR1+bYf79P++a11vKHdEp1U69zA8I93WqWxeJLS6x1Y36bMBCT1WS49g2TeoUhbpUVBQsu7/Y+++4+ws6/Txv0+bnmQy6b2HkBDpIER6FQRRVBQbYlkrKur6tf10Lbu6q6yuBUVFwFVUFOkCIkpvoSUkJKT3TCaZSaaefn5/3M9kJpPJzCQkEXf3er3Oa+Y856nnPPdz3Z92fTRplVVQ0tYt7niO6f7DOZ6wwT2WOcRw33GuUWpcY74RqnzYce63yipNrnC8U0zypA3OMd0VXu2Dbpc/APd2Z4lqqRRC+cVuSc6xWCC2svLwfz7PkgVs3sgTD4TWftNn0dqyb+3HXnnEt7mJTI61W/n464Ll9/zBJZmeGG2cM50nKWWYEcp78e8faNQkOG0I6zKMKws1oITSlbGpUCe6v4loQzaU0dRnQ2bxpmxwiRaEvwcEM84IZLdtFZW1VA1jxEy27S4j9UpBwjShZ9eBbGxGsFzm6s/RXLJVaSCuzo4c//kXzp3DiVNDFmcMY4aIrdxqUCUt42uVVm7lZw+rOna84vKt0j96MNTX7gcMrqAtE5L5wrnXa/dllFT4eJ+WX8xgSfP2SHyde9z1XaHb/yUjna/MSJv8Nmq+XNr5aZWZRjrfVvdojZLCYsrUOlad05UbJWOTrf6s2TNRzLD78eKqTVfnNDVmiUnK2Gy7JzR6UCnyGoUKvvVGOMcgr1JStMPTtrrXnkjzIWs8ZA0Yb7DvedyWHvffEBWuNM/D1vqEP+mQUyahoORDjnWrFxGUU2+0wFM2OMVkW7T5hgdsl3ax2aqkNO8vPdpuaG1h/RouvCR0Lxo+MjRs39HEmReE3rK1dXS08eRDHHpE6FVQLLLsxfC3sI85Dq884lu/jZsfDwPwxoeCBfjQ4n43O1BISnq3D/qbez3nKYf0KDw9WKiMU5dkekXoudh1foHw9kvT1R4Bgkw0AysKmcWF0kEIu1XUUr+YwWOCv6O8Rlfr6AOLVCy82vdychs3WdwIxQNOfDWSDu83C7JglWLUKikeCzPmztl0PBZeheh9cvMOhWsfVUIiWjf/wDKpBDPG8PzGYAQmHlru1RuXa87w9LogrJTv3H/ncUshByYZZ01TSABNxMPf3u7PVJyLDuPuJSF0KNbZhKVNh29LOj5ye+6xLaukIwR5tn1J9y+qMt00X9BuhUZ/6/ZZ3HjvMc67bfPX6GhlJvu4ya6QUS9js6FONMH7rPAt6/2sW8JM3GgXm+FrUobosFpRznDnGOI42z2+sxNEjcPM9n2VJmizXFzKGJfY4S0W+5i0dftwbUwyxGwjLLLFeWbsXJ4QM85gY6OkrA459dqk5e2QsVKTnKId0sokoujh/kchz/U/5PiTgkXXsDks+8X3OWYe27fx719keyN/uCHE+EaOZs2KIFCxYH4gwH3BK4/4RtXysfO73CklNOzg0QNZOb9nFJU02maOw403SaUqhb9DJ+lcKbgeb2nkG+t3fZCUBD3XvUX327ksFpp//92x+hFO+gR1k5lyUlDX/cu/HfDDxnDpKFry3LyX4iBxoyQcojiABqMvBwkTJfpR8igpKXgWabEYbz2SihTNae5fxhvmBiJ7cEUo1ztsdMhZeXwNF82lLcsdi5k0lOMnsXATE2t57aGMr+W+lzh3VshvWbw5eEOnDQ/34x2LedtRoQriN8+GZfOmBMPyloW0d+Om4ydxxFhmjuDhVZw0NeTa3LGItduD5ZfxS0kn2NNjKiYWNdwtt699/ba6x2SfMNIFGj2o08IqM8II59ruMW1R2c8wp5rqMzb6lRW+KW+7lDrTfNF0X9TqBU0eBjVmO8S/ydjiBe/V4gUlBSl14soi65KEGjN8WcoQz3mrFi+ISRjhPHP80EQf8pIv2Re5tsHKVUk501RHd2tMHBezQL2sgkpJBaVursyS7G7HOnAPhqat3P3HELs77bVhWf1G7uylL+2j9+/6flsD9jH145VHfDUVwb15x/zwV4k1fz+tzqKCn/me2Q7XbIc/uUXjXn/bAyHKvn+KljzzW5lbFWbW9d3GeSoWiHGgaCsEKbvuWZ0zK0OyzP7DQGzDuN3cdluXcd/XGDUnjLdNLwwovpfA8LLwd1s+WKsxDE1SHg8qSflS+DwZqShVRMsLJUaXBUH4v+xTf7IqSSfLudeBtIeTThTTX01rWs7jKIlh3BB+91wgtTGDA7l8/6FwzR99TbDMjprAovpAWJuagxdz+VbOmhkstqMnsGgzO9IMq+LMmSzZwnETw7JnNzB9OEMrWbAxENzSLXz61GBB1pTzyKpwLIJlecIkbl5IbSXlyWA5jqgJ4cW1kVZB3nwlO/rMmo2pFlO2z996m+WaPGS4s5QbI2MDqPVqlSZb5dtRy6+E0d6soN0aV8tGCR8Zm6z1I6NcaLQ3a/IoikY4T5mRXvRJTR7ZebxCj16KNQ5V5xSrXKXZAp1Tl23+otWLhjvbKt+R24dGl+1yOuR92yP+YFevWQntst0IcaDf4EDW23tty452fnk1rQepZPeVR3wwc2wYCdl8+J7vfiZYfX8nbNfk0V3cIHuH0oDayNT2+XkeP9nMz2dwzfRQuJ4uMqU8kNg31oXuHwPBko4gjPCFCUHgvCrBm4btJ3fpTuT0164npkKsZ6JGooyxRzDxeJSCu3PF3yjseUYfw9tGceQgckX+tp27G0M26htGhIf8ugxXb+CQqrBuLiLBqzeEufQHxgYi/PwkvrZ6d/nAvq8jpsxZ0r6rtK9T0H5Rrczr9Tdki9YrWLDzfVWKOaODAyWdY0dHsOrisUBEa5uCtdfUzjPrg+X1UkMQxxhezdjBwVqcUheUzJZuCdssawj5MHPHhH12RN2q2rJMHRaswbXbw3FXN7Kl2/O+WArJobNHMag87HtCbdiPXTwZLZECzYFDSdZmNxvhPEOdaLObxKSMcpGMjZEVSEKlaodIWy/TI3EobYO0jWrMFlehKGOQw2Rt1aLvdmpVpksaYrQ3qXXizuUxcdVmKmiXULNH4sspKJNQ3kuZ1RrbrdToFJPdZJHWyLUaY2esb29RGsC4Fo3rvdl7Ic+Sg9h57pVHfLkC67ayYHUYqbCt5e96Si8PJSVNSkp9xmYSxgvSV3t+4i5o5z3LQr3dO0YEy6U+xx+37Z3FtzbDJ1YG9ZePjQ2JK7/YEkoklqfDs2dhe5izlYROHw82B4m9WDEIp/dUCtr9qtv7JfwwWx+Jbo766acz+3UsvRsxDntDCFAt/8se9xPH3BqebwlqRzvyISb6ztHc3BBI70uTAxlWJ4JY/IdfCpJ+uVLY/ocbQqnGZycFctwb4oOEOVJOlvWHvdtwgEg6TtIJfd5DJSV5jylGVguBiDJ5/vA8G3Zw14uBeIol/vtpZo0MhJMrkClw5+JAVFPq+MsyypI8tjqQVL4YyPL5jUwZFqy9B1eG7XekQ5LKqlLYVzLObYuCKzWTD8u6zjNYezOGB5fo+h1BCCZbYH03ZbqYcrF+5OACMb48FYftHtVhtVEuUu9WlSaqc7J6t0lHZSEx8chF2a6n27GkoCQrrkLoNB8XV6koq9hPUkhCJWKytuy0NjuRtk7Wtt2sxO5YqN5g5T5lngetUSbhYWus16xRh6s86lvO9mMXeCByxc8wTLucr9v7xtElbfprvBwzWNxwxW4KUq80vPKIb2szjy2lsoxkEqUDlD598BBSy4v6EjOOmyGmrs9svE5C+uSq8JCOx4LV11PjtT+U8LdmHm0JVk62GKzFW3RNuK/pdhpPtvJ0W3AVNhfC8Qv9HLOkdY/1VV2okvQq+e4DsGYki27jpXujHZXCsj5QwE82hBjdV6cE3drFbUys4My6QHDrM8EaJBBhU77rWg+rCdtuzgSBgOQ+hTQqlXu/nPuV7JO/tA9Uq/BPYob2s15a1q06410lLG3giTW0RkS+qrFr7cZ2Hl3d9f6JNV3/L20Ir07MX9/1f1MHG3u4pFq7TRQe77afJ/dQwtbYzhPdPnuql/yN0N+vb1WckoYBeVT6QsYWDf5krLerMtVQ8yQNssUtOkmuKCNrqwrjxFVFBBiQUCWpVrvlirKCsuZWSYOk1O5mIXZH1jYlWZvcZINf7PW5P2iNr/iri812rHEatFtki/WalXCrJZqkvdsRLnOkEtZr9lsLFZRsl/ZXq7REpRCPW2+5cJOss8Mj1srtkgnbIsjhje31fCCmRsIceU/u9fUcLLzyiK81zS1P/L3PYr+iaI0wS9pzXVLCJElHyblrAPujdT/k12RLu+q9dp/Hdv+/pEsvVo//94SS1qiI+lV9rBWXcpa0nzNsNNNOY+hE5ryeUYcGN+ekE/jrt/o8Vkyw8r63nrPruGg4z7TwUjt3buXplhDn25ZjZNnu0cczh7Kig99vCSS4L4iJSTlZuUul/dj+6x0XU+YNyqJmpn2h4AW5KLmCMGe4c3Gw7v7xkFLmdfRROhSiYUvta2JLF4q2uN14lxvubHVO0uIFzZ7rtkZGowdM9VlDHBWVGgQMcYwK46x33c4ShSaPGOsdRnidNsvs2jy4q3tDqxekbTTS+erd3KPXZkjfKfVxL6Xl/cRTfuk5KQkZeelux8orut9KD1qtKioN6ZBTqcL5TnCvJ33UHfJRy+mv+tvO5tP3W+VvVu9Sw1fSrGizhFl9fJ8JKefI+LX+rMO/FxL4yr5uHNqO9D0gC16U9XsHo7g3Zqhy7xbvI15W0i7jeqWD2nUgocybxXtIHO2KlJiUrD95+QP5lYCCpMP7dM+FnmsjFDyhWNnIkPF0bKfhJfJpcmkalrB5IZk9u7tTMd41htcPD/WMd27jpQ42Zrh4JKcNDT0wn20NBFiVCP93ooizhgbS257judbg3o0ZpsJlA5AI60RSwqsULNpvbp6k41T7rpgx/bg5c9Kuknd/j+X7juC+PUlRI90snIOBlDNU+rx4n3qcGWn/pWAfc9q7IWe7OicZ6gTVZlrvOk09XIFp6w1ziuHOkdcsoUqdU0z3JRmbLPe1ncSVsclgRxrtTVLqxKRUmqTOSYY7W4vnleTkNAuNl96p2nQllBtjsKOM9XYpw7VapExSmZT8HkgwpyirIC6hhDIphWjdMikpSe2y0hHFpSSlpGy2bSexJSV2HqNzclgS6vzKlUXZ7PlIOOCYPsd13Ah5jyj2o1rz98Irz+L7H4iijQqWSpiwx3VCgsR5yl0q41r7u9v0gBAjUUVhgOVog48OJXYtz/X2aYg3hZjlnmftMUNV+pxC42WKjTfsw0mHON3PNgZCyxVpi+ZYz7exdFVw53YUQoxyaXvoWdkdTzSzKLrmjsK+T9HCgB+v2n9pc6Wce+x7/Ckm4RjVfhC5wfuO7RUsjCaY+8+8S5il2rWKlsm6U85dChYrOZAx97ikeap8u98M1oJV8ubvl6MWtNrkd6b7sqwGW9292zppay3yIdN80Uz/ttMa22G+Fb4urcvHm9VgsY+Z6jNGu9h47xY0YrI76wI7j7zWTxS0m+C95jhT6ARTkLFJs/nmmOJtzpSU8KiF7vCYYo+7tEaly5xnrGGatBqp1i/cJS3rnc5Ro9JL1rnOXWpUeZdzlUlZZKWCojGGudz5BqmyzHq/cq/Bqr3ZqSqUG2qQn7g16vP5mHLv01c7rpiRKn1eq8sHJqZwkPF/xHdQEPQIU04T61PkulqVrwqOlRvtt1l2jGRkbOY724olSEWGcX57ILCK8Yy8kA03UOyglCdeSXJQIMNOQoxXkqhm8BEUOgLxxStJ1pDbQSmK9+Q9rWhNn7VnQXrqZFW+q93nFC2zLw/vbCmqfumBdNQDsxMlvccnWwsh9f7l0kYgvxlq/EzaD2VcFyWb7M2ea5S5UJUviTukXxcnHdJ+sEtSy/5CTIWEo1Q6UoUPK1gg5y9yHlTwkpJGLze5JCAu/Km0eAAAIABJREFUbowyb1HhCvGoW/meEOJotw5Mk3SA2OR3mjykKCe9h++yxUILvUelyZIGy9khbc0uMb9OdFhpsY+rMFaZkYjJ2y5tg0I3sYOidutco97NKowXVyGvVcYmMc0+4/1u94jlNviCd1lgpdU94oZlUuaa6jYPO88JHrfI4aa71cNucLehBrnSJW7ziAZN7vWkT7okEt3Pu8Tp1qp3l8dc6RLHOdR6Dc50rC+4xmbbtETXmPeEog2RfF7vCK7/s1S7SrsvKkaScK8U/B/x7Ud0ee53R86fFH20X6uPUapdJeUUadcpeD5KEtnTwyUmeKxTURZctZjBYoaJqZWPPWrY2Y3qTiWWYNtf2Ppnxr6VmsMCuW3+HZl6JnyAupNJ1bHldtqWMv69lI8M5LfqOxTamXxluNDqGWy5LRDmhH8KxJHbwdofU2gJqfVZd6vox2KJSSpzkYQZMq6RdXf0EE/v4Rvt1KEPTWBDq55BYmrFjVTSJG/v4sQ1Vcw7nHsf37uyjpKMoIXRNZQ6f8dK/58yb5R1k6w/K1oZJQb09luWiRsh6Thl3qHMWajpl/SCDXF7lE16YB4ssej7jhku5jRJp6rQrGi1vOcVzJe3SNE6JduUtAvu+j3Zzp33bKW4YRJmSjldymuj2FHfndpLSoqWy7je/vSMFLVr1780XkH7Tgmz/lCS1WG1jn7FDUqyGmR7lMMMVq1WjSXWqNekWZsx6nYjPmjRrl6jzbZp1GyYIY4209mOs12rIWokJRSVNGvf6QqNi5totAc8Z5tmK2w0xVjrNdhoqxU2yHQLvxStlvMXce8dwLh+i4RZ0q6Rc280UcnY87iO29O4Ltqi4Ol+vseB4f+IrwcOmcHWbWxr7H/d7jj2aLY0sGYPLu2CJbL+qMJH9dV9OtxIg5V5h5QLFS1T8KKCtUq2CwM9Gd0U1UKbmqHRzTE0ej9YTLWSdh2DzzPmkkYbbyRRyZi3sv3xYK01PU7jA8EKLBUCiSWqWP0dChkUqf9DsBYnvJ+aOYE8ix2suoppn0Ocka+jbQmb/8DkTzD0RLbeE6464wblLhYzVl8KEKHJ6GGqXKXCp6JrXqakPkpZj0ep7VXR9dWKq9157WHZIDGVMn5p8oSnjBtZVCiycj2pJNMmsGwNLe1Mn0BVBc8uYcJopoxj9lSeWszhM9jRxsKl+vV7ZvxGTEqZS3ax5sPvmJJwhEqvUuFKRasUrIgIoikizTJxwyVMFTdLwiRUDsDK63RxLtbhaxGhHnh0kWCtuCMkHI53oENRo5ItijYrqhc0Q1uirMuicN9WiRkqbpS4cdFrlPC7DjSdtk2H7yj004j2fwLSspq1m2CUnLzBqm3dQ7Z06MPYRSlxMReY5z7zLbHW8WZH1BJXoUxSUoUyGVkbbTXVWEutM8FIjwo9UIs7U126IyfjOmUuEDOqz/OPSUg4QrXvRTWmneN6S3RfdI7rajGDuo3p7uO6RkyltB9r94z9McH7X018lZXMnU11FS8tp6WVKZMC8Q2qYfas0Odp0YtBG27unNAjalEUS29uoXYIHR1Mm0J7O5MnMnFC0J5bv4G1O9PAczKulnKWhFn9DvKQ+DFE3DGSjtHVp6ykO4H0l/CQqKF8DIOPpJRj+2MUs6z7KaMvoe41rP1JIK5iNliAhaiQuGIikz5C+wpSQ4mlKKsjsymQX8easF7ZaFoWUUyT2RiO14mC56X9QqXP6q/VTBdZTJYwWcm5e33NAXHzjmDUsFACOGUsMyaxpZFXz+VPj3D2CdzyVwbXcP5JPPMiFWVUVzCijnPmsXEzmX4qE0padLhK3FhJp+x2brHIuokZLm64pGP11nNu4A/9zuOWlGzU7rMKA7Q+DgQ6r48aCTWYuPOzXa9zb3/D3lGSkXaNjF85GAlzf29k5fzavd7sdPCERVb1Yu3l5a2wQYt2a2y2zQ45BU1anet4R5tliTXSsg41ycVOVaXMFS7wc392k/u934WOMcs2OzzmhZ3xvlIv33PeUzJ+qcIndvF29IbwW5dJmCph6j49ywL2nzL+/2riG1rL68/n/gc4/1xu+HUgrjXryOV57Vk8MZ+zTiObDVJOhx9GczMjR7BsOXMPY+xocjkufTOrVjM0KrfauJlf/bbreAVLdfiqaj/E0L0a/F3r7t0DI99M+yoa/0r7ylAhUMxELskfMfZS6k4KxFfKBaswVUu+lZpZFHPBkhs0N+wvvYG600gNp/oQWp4PxFgzh9bFVE0P7tRuZyDtB5KOknJun9bu/rpmyOZYswklBlUHUluxju0tgQxXrGPxSkYPC5OUdfVhm9ccGQk4l4KV2F8ydtwwReu0+bhqP5F0XL/XuK8P/U4E0tuizWejBJpXTuykO3a9zpev91jSIeNaHb5uoPHvRIyhFSHpaccAM+sHlYXC/95oNRGjMkm6EB7D2YPAvfMttcRaKUk7tO2W2ALN2v3cnQoKVtsYdWyMaVb0kOfk5ZXLKyhYptW3/do4Ca9Tbocd2hV9ww3qVChoV5CzWdrVbtmljq8LWWnflXSkpNMPyrNsf+J/NfHBuvUsXMwhMwPZtbaFVvf5AitXs3gpZ5zC5i0cexSPPE5bO9XVDKujopzBg1mwMOxn1EiaW0O8K7WbgVOSdbO4USp9BUNe9kOwP+RbWH8to9+AONsfZeu9IYmlclKw8Db+d1i3Y01Ken3O5E+y6Xe0LGDoa5j4QdqWk14XSG7QEUy+gmx9INPWJYy/jClXBpLd3iO8FlrNfEq1mkht/8B2dobVG3eN1S1dzcxJgfgad7A6mjRv3c7ydRw/l6Vr2NgQXJ6LVtA+gLromDokFSzQ5gOqfTey/A6M6EKIb63W7v/JutnfJfv3ICMQfYO070n7wV65dYeUc9lhDK/iiw8F9Zm+UJnkE0dz9XNs7UUt7cRxnDuF7Rn+soZnBtAmsqyKQSMQo6UhtJzsDXUTqRxCcz0tPaqtWgcg3ZaLYscnSzhdmQTulfVXLd6swhHKFfBbafVy3inlFDE1Kt0uY6mMiySMUa5Chau02dhH4lLRBm0+qdo1A5rwvZLwv5r4OjpYtoJ0OvydNCFYcnNn8+TTgfja2li1JpAczJjOilXBGpg6JWz37PMcc1QgxNVrogaLsdDgendkpf1YSYtKX+o3e21/oHl+sMxiyWDtKbLhWmLlwcor5ULNWqrtUqu+8xOxsuzO9ZZ/LbISs3YaFqu/SzwV7UtYvvq/iJcFd2dvBkjBEq3ep8q/RYXJZQf0uh9fsOv7Eh55rquYe330wMoXuP3B6DcrhfWeWhT+j9OPbogoBlGuJK1goVbvUenzyl1qIMkpA0VwD+XlPardF+UjMeQDiaIGRRvFTdwZ2zuYCNeckfeYDt+Sc7+9rXFtTPPHZXzimK6zH1LO0aMCyT23hQ2tVKc4fkxQQ5o0JEiujawKWqeH1AUSfGZzsByXb2doOeujqo7R1Rw5kqZMIMJOUYhYjGnzOOfTjH9VOP7mpcy/iQV3BoLrRNVQ3vljJh3FLV/iwZ/u23c2RMxbVbhRWpWYS1WYL+dYKY/IekhWc1ROf6eMGjH/qV06KpO/WdogMR9QZa6kjX1IKELBQm3eq8o3pZyjv8SkVwr+VxNf03bufzBYBvdFpTXfuxqlSB5yZfjs4Ud5zztDbG/yJNo7uP7XYf1Oq2Lh4uj/Xd3We0BGxnUKXlDhM1LOjhIzDtQNkxLPTSdXLWaNkgalQrl4+wwxRQXLxJRLOEy8MDtSHH4JOXJ1ShISJipar6RerDBYrDBdXFOkSlOkEOJ+3REzVtxYxeiYRcu0+YC8d6vwT+KmC8Ht/X/dvTn/uiuY9Px8lzZPpT3voydCcXtXnWLR2qiG728qfFzSkV4OyXemKxStkXGdtJ8qDaS7+n5A3qNavF6ZtyhzvoQZ9i4JZd8QrrlN3rMybpB1i5J979DSM0t3WCWjqqlJccYkvvAQ75jDiEoa2pkYzXZeO5XjxvDQukBmsRgVCZozTKsNn9+6jE8dy8IGjhnDhEHcFHVQGzeXd1/D6Fl2dhsfNplZp7HmWR6+lsX3kmnl+Hcw82TEaNy39nsIU63xEo6Xksez8rJKrtXuEpVeo8yPtVumIK0kp6Qt+sbHi/uIKmsVDBeXGuDvXLBYq8uVe68K7xM3xYEa1/sLL5P40koalfpMsNizwOr+R1CfK/ah9hDqjrppz5V2/VvoqdUluEB/ezOjRvD4UzT0MgaLxd2360TnDbBrsL8o70mt3iPlJOXeJukkcWPsy4Oya99ZJTsUbZD3jJItUs6T8moF6xUNlfNX5d4rrg6JbunJk6WcKW60vCdk/UGFjyGupFHekwo6VPikku3iJsq6Wb5b25VOJMxS7p8UrRR3ibTvKtqgpFHa92TdrszFyrxewqGCJmNsL6+786qLaFfUoGClnAcdrLhXyK7tqUTfLus3cv6mzPnKvFXSkUIHjv4fCF2/ZbuCZVG5wm8VLHFwXZt5BQt0eKFbnPY0SceJmxrdPxX2/nfbFeF6i0paohKJR2XdIe+JaLzuX2xPh3hfdYqxg6gt54iR/McTwUJ8dSRDWZ7g6c38ZknXthtaqasMLtOpQ5gxNNSF/vfiQIYfO4pbl4eClZPex+hDUKJtO8V8sOwSZUw9Plh321aTbmXUzOAS3fgC619Gl4IWJasU/EnGKgUJIU69RdF3tXmHSqcqs0yHnNBHYZCYdiVzJOXxG2mz9pIaSrZK+w9Ztyr3JmUuiGpQB3k54zrQcoOCFdFzZv+M65dFfFm3yvdTV9F3DdrLQ1JCUmynNl3RJq3epqeiQExMtTJtMkryClbt9bF27AivfcFJpiuTdF83aaUxhphgqPnWyPlTRDwTJR0RSQLNFjdWSOmtFLpMd1YK5gVNwA5BO68hShVeruAlRasUbRTKH/KSThSKZx9W8KKY4ZJepd1nkVLl6ztJMuNn4sao8BFZt4upk/UH+ag9S9KrJcyS9VsxQ6Wc1SvxheUPy/qDcu+XdLKsG6NPi4qWS/t3GT+VcKikYyUdKW6KUC9WI6Zcl7B3ETklaUEAuylKm1+t4CUFyxWtVdQgJD4crISPcntSpinZLOPnsn4XXeOrJR0rbpq4EWKqdf2ueSUd0cRtrbzn5T0i73lBXm8/uTVTyTDLy+8NgRaVbJZzl5w/Rb/NGAmTxc2QMF3c+OiahkTX1dlZoXtvthJRJ4MwUdmhZLOCVQpeiF4rlWyzG8HHo14hexIeTSaidSLk872uG4/xoSPZ3MaSbZw8IVhjMV1dKzo3K5Zo6ebpG17Jp4/lt0tYtSMQXyy26/qdHelrRjDrVMRY/Gdu/yqZNg45lVe/nfGHk0gFwutELs0j17PjZRj0zUqu1+GSqN3XY3LulnGxChMjObNfRvHCtQq2KPq0ajdJWyjvFGU+qso6BRv2epJVVLREh29I+3GPcT1Z3HDBJu28Lwi/c05JRih96RzXq3qM663sxxZVL4v4SrYp7EODxL1FZ+VQqDyJa5NVUnK8KYaqco9Fkb5ctlfdvqGqvMsJfua5iCSL4mKqogaW7TI7H5MVUrscA8olJSWkZRV1eTKLSjvnMsXI7q1WLq8oHcUi4mJW27aLtTfLaJ9wpipl3ucGWXlkFS2XtTwqRi4T6vRqBPdSmfAACXp5gQA6BPLr0FfBcNZtSpqUu0zBQjl/EWZVWTHFaL+paJ+5aH/JaHlG924D4aGWV5LfqczSO6p1SluVtEbX0ROlyJJ8JCLPUNQcHqzVQq++zlu084GZjgiiXZjLHphJ1UARk+g3kaWkRd6TkVp9Mrq2QULtUrlwR+WEVk4tkZfkAIj7VlXwL1eEoPbXrg5prHuNUnSOLYpeYqdYc0q4Zzt7LJZH19Z5H4VREu6dTPT7tXe7d/uYqFRX8vkP0rSD790QXDDdEYvx4Us5e144TqHAv/+MR54xpjokpIyr4aTxwYKrSgaSmlYbXJfpPEsaufgQNraGRJhSDxIkZHSWJUjFmTk0ivc1Bcvw9dOD9Td/c2jvNGosg0bS2sAdX2d5NDdc9zxP/ZZj3szxlzJmVrAAW7fx2A089FNKL3OO87icZ+UkxXQIxQg/16FCTFanjDatSr6lTbnYzhjfV7TutBJ7+0VSgyhku8X3e0VJomarUv4h6fRDwriu6jGuu4ivaxLf+Sw78OP6HyLGd6gxzjHHYJXKJX3f/Saq83GnS0k6wgT/7XGreyHhEQb5kFO8zquMNMijVrjTQuea41SHqFLmz150m+ccboJLHSch7jnr/MZTRhvsCmfIK+iQc58XpSRUK3OXFxxjkknq3OI5b3GMo0xCyS897nnrnWCaNzvaPRZZFxFIq4wbPektjtnDFYegfnhAvHxXT9wkRZvlPSjp1bJuUtIs5RSUCYXGTeLGSTlR3AQFq/XWGzAUn7Yp2R4RZe8Tn4L5kk5SVC/pKFk3DeBMC8JwbD1o9trBR17JDv23bDoASCY58Uha2na1jvYLcuyMGO1n1FRx7klsbuDqG3cnvlKJ+S8Ewnv1EbzpHG64BYET69v56fPBJVko8ePnOGEcW9r46qPBqrtuIadODN3jv/xwyNr88+pds0C3tPPDZwNh3rKMbengGv3e/OAefXYLD0d1u03r+OUHgxW3urucaClYdH/5Pk/8Olh85dU0rWfL8j77Le8VMnSbzgca6W1U5YU7shP9taGceBFNL9D4bN/rjTuHdD31DxPGdUs0YeqGGENm0bwsZJcfTPxDEF+davNM80m/0yytVcZ27R61QkbejZ7SuoeeXI3a3GGBKYb7vvs1R+s9ZqWnrTXbGJc6zl8t8R4n+r1nLLHJl11goQ1ONsNyW/zOfN9ysXFqJSXUqgSjDDLNSDONcrpZvuVuc41zuXk+5SZPWeUwY03o1kttvSZJ8YF7veMxjprIkRNYtoVHVuza2bO/zdVKmieo2f9MSYu0H0k5D0VpP1LUJO274saiXMYvUJB1yy56iEXrpP0kIs2CrNt7PWaIs5VLOUPOPfKeGfD5/o9GLEZ5efibzfYIKv8fekVDE+//YqgvaduDu+vRZ8NrfT1vOGvn4o2t4dUdrTnWLdl12fZMILPuWNdDi7skWHTze2gur21h7dJdl7U08OwtfVxTidat4fVKQ/kwqsaELHDItwdyqplE62raInWqRAWVo6kcRbycbc+G8qnqCaS30Ly8a59lQ6k7PGSIN71AppGhh3Hox1h+HW3rwr4rRhJPMvgQso00LiBZTd0RKNH4HPkBiuj3hX8I4oPlGnZaTASBpDZZGTnb+yhmLShqlpaR16hdVl5S3FlmO9QY5ZIGqVCjQq0qS23WoNVGO0wyzARD3W6BJu2W9KIy3hm0nWComUZ5n9col9IuKyEuI69DVkU/qiV9Yupwbng3h4ymvpk3XcOjA297k/fEbtqVIUvw6l2W5br1GOva9tEeS0oKnlXQz5RPVs6dcu4c8Hn+j0aqjHknctEbmTkzWF8bN3Lvvdx1B9u397+P/X5OyVCIms2R6WWuH4uRiHfFA8tSlJcFiyuT2d0XFo+F/SUSQdEh3Yf9EI8FGaREPNpfdvf0y87ji7HgpZftA0xVkIzC/7k0+e6nF6NiEIOGh79iZDtoa6S9KSSm7Cti8WDVVdVSMTicQ6lItp32HaETV74/U2uAx6kYRM0wygeF99l22rZF1zDAOdbEixg8jcEzAvFUjODpzweCm/l+Xvop9Q9RM5ljv8P6u6gay7AjWfxflA9n+rtYcyvr7wgyh4d+NIhiZJvJbg9kWnckg6czdC5igfimXBKO27SAjhp2LA3blqJzH3kii78bhDVeDv5hiK83xbiCogopcbFeP+/atigpLiUuJ2a4Gm9whM/7o9GG+IjTpGW1yRpjiFYZI9So12yrNuMMVSZpnFrrNMrKG6xSUtwkwyTENWi1xGb/6T4dcmJCBVJcTEJcQrzf89wjxtYyoS48LEYMCkS4F8QXlyAqXKAkLqFGnbiEVk3y3eJJccnos7g22+UG2N06pUKFGinlSshJy2iVe4U2ojyoKCvjIx/lyk/T3sbGTeEpdMSRvPY8zj2XT32Shob+97U/UCpx5gm8/QImjmXLNn51O7fdv2vc7/BZIXb2rWuYNZV3XcT40UGh4fo/8puoaXIqyanHc+FpzJ4eYolNzfztSa77Y9h/J2IxXnUIl70h7L+ynB2tvPBSWPeFbmbX7Ol84YOBTGH+whC726vEnC6c/tEQW4NHr+evP0KMUdM59q3MOYu6SYGkYrFAji0NrHk6xOVeejAsGwhicYaMCdmbs05jwuHUjg2ElIjyi3LpQKz1y1h0DwvvCi7PvUWyjElHc9QbmT6P2nG7XkNzPaufYv7vQ6wx24/oTTwVXJSlEhvuZcpbgnXX8ERwYcaTXdeYaWDZtQyayqwPhmWNz9K0qGs9AtEnqmi4J5BZqcCGPzHqRJbfEEiRYN1tfZIVkahG9cRAjk9+MsyzjvsOlWO6rM59xT8E8YUGHbvHRJ6zzoedqk61X3liF4uwOxq02K7dF5zvr5Z4xAprNLrcaxQUrdSgTdZvPeU95snI22i7563TrMMVznCo0aYa4XErPWOtc83xZRcYpMJz1llko4U2+KxzZRU8ZJl7LfImRzvRNAlxHXJ+4ylpOVl56zTpTbdxN2zazvomZo5i8w5eGngT3aQyr/c548x2t/+ywWKv9XHHuEhSygrz3eabNnjREKOc70qHO1dCynqL3Okqyzyut1B3pcEmOcIcp5nqWMOMU646ilC22ma9JR403y02W9HrPnYiVcarjmPLRhobmHss4ybTvJ2FT7F5/e7bJ5JMnskhc6mqoX49i54J23dHPM5hx9DRxooXmTCN2UdSXcOWTSx4gu37P21+J44/no9ewU9/wg3Xs3VreKrU1HD6GXz1a7zrMq769t61hthXzJnOFz4UiGTZGk45jqu/Egjrl7d1ncOoYVx0RpAzOuFIlq7kqYVMnRDIrhNlKS6/mDHDeWZxkMc58lC++CEmjOFT3+wi1KnjufZfw29y78NB9WHyOF5zNPc9tivxNW7nwaeYOIb3vDFYf/G4fS3pqJvElOPC/w2rQh3d3PO46KtRrV28q96uE7VjQ/H5UW/g4V+ERJX2fvRba4ZzxscCEY2cTrJ89/12Yug4xh3GERew9jn++EVevG/gxm3t2FAgf/zbI3UYvV/DhCM45i3B/XrnN0I8cY8oBYuqGIlb9IVsc6Tx2/mT9HKdpQIvfp+xZzD748FCXHvrHg5dCG7QTsRT4bvYaeGVwrKXi38I4nvBBkt7cTM+a63P+aMKSZv7kDFqkfEVtxumWpN27bK+5g7D1GjSLq8gI+9BL3nBBpXKbNEsq2CxTT7vjyqkvDVKRlllq3/2BzXKbdOmoCgj72p/M9IgcXENWmQV3G6BuyOl87zizvYemzX7nr/sQQevB1Zs5T03cPREFm7gmYFPd+ISZjnJHKdbZ6HDneMC/ywZlXyMNUut0X7qA97gi05xWWQhhs9Gm+G73mSzXUfKcBNd5gdmOUnVHjrLT3KEI53nZJf5lU97vi9dyZrBfOG7LF0Y7vRjTg4jeHAt61fzb59k/kNd61dW8c4ruOQDJFNkM1RVs3IpV32eZ7u5aFPlfOwrgRAf/BMf/DyDh4btYvjnd/PwPQP+TvcaZ5zF6tV877u71sS0tfHb3zB3Luecw49/FJYdaAwexBXfCBZesRiI5Xff5aPv4E8P0dDtyVNdxanH8U9fDqRWKAQLrDtBt3Vw5b+Fvy1t4bPBNdzwrZBpOXIY66Pxe9hMpk3kvZ/n5nvD7ZCIUzuY1h6myKYGfvwbhg7m7Nfs169gxBSOvpg3/XuwzAhSYu3byWUoq6CqLrhHY7FQf3f6R4PL85Yv9e2aLK8OlmVn4XqpFAz8bDsdzcEKSySDPFnF4MDliRSTj+EdP+SaS4OF1h+GT+HtP2DO2cG6KpXC0GnfEY5TKlJRQ+XQcLyqWk54V6gt/O8Ps67XBtI9EP3M8VRwQVaOpmYqVS/pleRiCWomUjUuvK+eSMemYBE2LQ5u0yGHhM8K2SB8XzubtjV09CL/1lEfYnqjTwnXV0j3vt7e4mURX2WCaYMYWUG+xKYO1rUFAddOxDCqkumDQjrwmrawTvdmoLVl1JWFz8ZUhn0WSqxoCfvMK8r3kqpfEqy5gaBVRms3t1ubrLYeGZMlbNOGXR8+jdH7zZrtiGpJmrRr6hFbzCva2MMybdmDq7CopL3fHKrOlUs8viq8XgaO9QZlKjzoeo3We7W3GG+OWU72Jl92hPM95Y9WecZcZ5rtVBMc5igXustVu+yrQ6tKg1Wo0WiD9RZZb5HtNomJG2uWw5xhmAnGm+1tvmmjJRr22JcsFqy+cy7mzt9wxZto2c5hx/LZ/+DKf+WjF9O4JTxNXncp7/tn7ryR3/yElh1MO5RPfI0v/hcffzPru31fyRTHncro8fz82yx6mngiWJWLD2DiTSzG8GFsqe+d1IrF0Mvq9DOCS/RgEN+qdTzydJfqwtpN3PUgn7yM6RN3Jb4Ybv9ryJrsJLt0L+7rTQ3BFV9ZERTBU0lWb+CYw4Il2YmNW4L00dvOD4koC18K77cd3BjnmNldpNe6lad+x3O3sHU1+UwoJh87JxShzz4zEFOyjHmX8/wdLHtoz/tuXMczN3POZ0IG58oneOmBUJjeXE+uIxBV7Zhgcc67nCGjw60yYlqwFq9/fziPPaFqaDj/OecG4iwV2bSER34RXLLNW8KyqtrgBj3x3Uw/MVzH1Ffztu/x83exrZdqpIYnuuJwbRtDnK6YDbG47S+Gcx80jR0vsva2cJz0lmDFxeLUzglEVioEkuuoD+7KwTPJNbMyUr3KbmfVjYw9Mxyzo57ND5DplvCTb+XFHzDhgvD9vPiDLrfoy8E+E9+Icr5xFCeMIFMGNYHZAAAgAElEQVSkLB6I7d9f4Pqol2McF0zgs4eF2plOXL2Ua5eHOhg4awzvm8ENK/mnmQwtCzp6v1rJV54LhDTaEMebKinhIS+pVeVwE6y2VbOOnUkkow1RVHKoMZbYbIGXof/TAzd7dt9idK8AxMRM9Cq3+ZabfVVe1krzfcyNKg02z9s96jd+4aPSWjzjdp91l2EmmOkE90gpdNNJbNPoTt/2pN97wV80WCMr6mckxAqnO877/dQ4hxpvtrnOdL+f9X2iOxq59jusitLkNq5jxpxAcocfx1/vYMhQ3nQ5a5fzw6+xNbImNq6hvIJvXc+5b+Jn/7HrvmuH8c0ruefmnedpaQ9Rz/2NUikIuB53PKNGsaFHZ++KCo45JiS6dOy/At0+sW377tmRqzeExJURdbsuLxZZvqZvF2wiztGHcclrmTuTqsrwNJ4wZne/2/NL+Mr3+fi7+OP3eXoRv78nEG/DAXQ390BFTbDMtm/gxk/w/G27lxJsXsqyh3n7D4N1GIuFxJGj3hhiZXtyR5aKPHId29YGEtq6qncS27oqkOLa53jn1cFFGosx8xSGTaJ+D+0GYzFOfFdwj8bj4adZ+gC//hgbF9vFqdK4lvULQvzw9f/Cay4P5Dd9HmdfyU2f2d16bYwswdbV4e+myMJac/Pu57LpvvA308jG6P91vSR6r/69Lv2NThTZcE947fxOntx925YVIaFlf2Kfie/0MZwzlg8+zrONoRB0+iBWdjPADhvKvx7F79dw3fJgxb1tCp9/FUub+Vv0vCpPcOxwOgp89XmWtzAkRVu+63saY4jx6rxoo7MdZoRB7rLAueaab5WpRmjWoVmHo0x2m2dd4AirE42ay3PBakrnwpc/egjThoeSgOUNbOs2y64uZ+ZIBleEDMqV28iGlK60Phze5UmG1zByUNg2Hgual9vawn5a0gMXE0klQqVsbygJ17EnBYs+0KHZ026TjyzNFebbYpXJjlCQ84hfS0cWdINV6i033ER1xilTGYkcdeEZd9rTRRXlveQx97vGO3xHXNIUx4i5Vm/9vXZiwxrqu5FDqchzj4ffbcZhgfhGT2DidO6+iW094p2Lng7LjppH6nvkuo3q+g1hXwd78nLP3Vx+Of/5PX7+M9asCb6vESN4/Rs462w+86lQWP73QqkUKUX04r/qL5nk1OP56deCNffrO0LcsK2d9795dxdlNsfPbuK+Rzn35FB68O3P8o4Lg/v1xf47oO8vFLLc8+0Q9yrt4RJbtvDnq4IKSycxTTkuEGdHH00itizvJ44WoVjg+ds54kJe/Y6w/0EjGDF1z8RXOz5YookoQ7VpPX/4LBsX7fk4zfXc+pXg5px5SnB2HPtWnriRlY/3f577Bft72I2oCkri2zqCFM9eYJ+JL18KE72hZbTn2dzB6h4m6IUTwni6fjn10QTzD2u4bDrnjeOBzV3fRTIerMD7+pDrSUa5kaE1S0kiKiZYbaujTTbeUNd62BEmSUSaiKXTZvK5U1i9jU/9nuMm8/XXc8ioMJt9bj1fuDVkSU4exjcu5KxDqSyjqZ3fP8M3/rQrOXYiEQ8ZlmfOCtvMHs2wGiqi2FG+SHOalVu5fQG/fopNAyhcfs8JXHps75/tSHPl71mx9xmArbZp6laTl9Zih/ros0b1uh46OVmtkSu4XI1kr7Jc/d3JJSs9LaNdpUEGGS4uodAX8XW0Bbmp7mjZEZbVRtZIzeBg2TU27D7t7minvTVYd6myXYmvo430QbKqumPRC3z+c3z5X/jVjcGyKxWpqAzNHf/zO9x+28E7n7raYJV177s0cUwgpa39ZG70RCIe3JYV5Vzx9RAHJDzB33Z+79sUS6xcz49+zX/fxuvP4Kr/xwfeEhJh9mFSty/Y9GJwce6J9DqxcXHIvKwZHt4PGU1lbd/EtzfIZ4LFdvylIUaWLGNwH43NDzklFL53xg+fuZk1A/DWN2/mgWuCqzNVEQj26Dex6smXrxZz0FGZ5FuncfJEPnkfty/rf5tu2Gfi++smbl7D147kPTO4dS23r2NtWxSvjjGnlrFV/HxeV0wvGWN0ZXjFY13Ld2RZ1seNVEKVMjXK3esFg1U63AQPeclG2z1giRoVttjhbgvMNtY9XtAyciwnz2DWKG6Zzr9eFAiKcOecMoN/fyOX38CXX8clR4fP4jFqyvnIKWxt5Zv37D4gj57Ide9mxoio3ihCsRROuDIWrL/xtZw4NZDjh25kVT9VqxPrmDe9S/iv+yx8ays1ZXvctC90aJHtFpcsKshFMcsOzdK7JAiVdpYidBZj7B3CpCSrQzGSH0pI6rd1Rapsd1WRsmhZJvIXZTNRkkXl7tsnkl3JLsVeRvPByJrsiWKRW2/huWc5cR7TZ4Q6vrVreOwxli7ZnewPJKaM57hX8aeoNcnIYSEJZeV6VuxlnngsFiTFMtmQzdmJqRNCpmZPDKoOv10n6Ta38rcngptz+NCu/lAHGKUSLz20a2ugPSHTxvau+aKyyhAD3J9o3hxcrfFQeSRV0ft68QSHnhHclYSEmYV3DbxGb/kjIQY5akb4qg85JcQB216Gl7myjOoKtu6nicCAcPpkXjuNGxZy397nPuwz8TVm+een+c0q3jAxxOYum86VT/FgfXi8JWNsaOfXK3fvVLyqdVceKZaCFbknpOXMt8qfBXu+QYsVutxcz3eL5b1okxd3tm6JUrbqqvncuWRyfPL3wZ344VOYMoxjJvHVCzhjFr97mvuW8KpxvHce1WW8+Sh+/gj1PRJp1jWFIt1iiTVbg/X4/PqwPFcIhHfWocybFqzAM2fx6TP5xE19K6/c+BRL6qmrYlg1E4byxiMZ0suDfi+Qk1booYHXWU6Rk5HfzZUbPuurF1tMwmAjjDLNSFPUGqPKYGWqpFQYaqwye/GUGDGGQUNId0scmjg9kNm6yCLdsomt9UybHcivuxU3ejzDRvLUA2T/jq7DniiVQmbn6tX7tHlc0iDDNasfWAlMX9jWFEoNjjmMrY3BHTl3Zsiqrd9L7d18gQee4vxT+fJHuPuhYFFeeFooTO85+Xjb+Vx4Ok+9wMb6kAxz2v/P3n3HyVmX6+N/z8zO9pJNsptseieFkBAQEAggRaoNEdQjigeVo+dY0XNUrOd71KPH3j12RVFAiiiKtACBUBNI773ubrb3ab8/PrPZkt3sEhLLef2uvOaV3Zlnnnl25pnn+tz3fd3XfTqjRnDvI6Sy28dzAnGOHxPaKipHhhrkO6+moYmde4NTSyrN2NGctSgoSc9YGJ576TmUlQSV6VMvBAFPL2RS7Fw+vEgnkw6ClG5EolmCOlpEssbYkWwLRTREev23GQh5JVTN6VkLN9eEaHS4aK6melMgPhg1KbRUvBTimz+Zs+fy1UFaFI45iuJcMYObV/OFJ2h/8YvGl6Tq7EjxRA1P1vLDTfz4TP5lFsuqSWRVmSeUcc/ukAp9KdjsgG1eQoNvXk5IS77+f4PlVwSN7XznjaGe9vqTufsF3nMLDe0h2pteweUnMnlUuPUnvv2NfOG+sO0jm9jTQGe/D+GHS7npUt77ikC2l53I1x4MtcXBsGZfuHVj0kgWz3zJxJeWGrS+FpLHL+aCGlFlpvO83UKXGW2yPEWHWiG699q97bAxYQrnv5o7fxbSlJXjuOJNQcDyQtZ9pmYfS/7Aa67lnMt4+B6SidCi8Ibrw1XhgbsGjvj+QVFqtMvd6Daf7hO1vygkU6xYy9Mrae/k+qsYVxlG0n/0K317+Aj3L11O9RBXxV//IbQjvOZ8Fs4J6dLb7wuKzXe/kfZeC5DVm7jg5VxxXmheT6bYsTfU9+56oGe7vFzecAmzpwZm2Jrt7L7q4vC5rljL06tCoW7aRN7xhvCcaIQnnw/q1GkTQ3TZ1HIY8SW7qN9r2DjapUY0FiY1VEyjcnqYsl5SERxWcvJDp01OXlB4dkdxR0JBaTC/7kZL7dB9hb2R6Ayim27kl1A2rmcUUl6cU6ZTUUZpAfetoLqRmeM4ew4NrTzwAi3tLJwWti3Iozg/fMtPmsKpM9hTx8OrQpwxLOTFAqFFIsE1vD0x+ECSZJqvPhW859qOLlNy1MQ3pywEOvvaw3F0pgIR9j5B7t7FNVN5zwn870aaEyE1O7Ew1ANrXoSpx2AtDcNGJBIisu4euAwe3Ux1MxPKw8rxN88G0oOWTp7eHoivKJdxA/SqZYQa4JG+FXVtfGsJl88PdcUxJUyrODLxHfY6xyb185IjhV6YbbHrfNNE80XQpNYOz9tvswb7tWvUqc0IVS73IbmGSdoH9vDGGzjzghDVzVsUIrsffIHt2aVtKskvvhnaF276elBw1tcG9eeMedz8bZ559Jj9rS8Go0020Ylq7dSuSbNaZSpV2yZXoRHGqrHdWDNUOUG7Jls8IyNttElKVcpXbIuntahTqtK0bP9okfLsiK2RpjtVUpctntFpmIX9tnY+/KWQVk2mQoRVVBiIqaXt8PNs+RqufO/Adma90dQSHFW+/5tw5WzL7i+C51b3tS57YkVojSguDI3vqRTNbYe3SLS0hXrfYGbaqXTPcT31Ale9b/DjG+D4U8kwAPZ4IRYPbQSnvzmMIho5kbziQISDNbMPB3lFIdXajY7mF2l3lgkWZt2IxinqsRFWWsh/vJ6fP0RhHu+4iB/dz3sv595nmV7FP53L75/m3Zdy21IuWBC4YHIlbzufu5/mwgVhnX/PUP2IBTlceQJXzQkTgKMR6tpZcYDvLw/jL7oRi3DWBN6+gHmjw7ab6oLD+EPbe9oEhoGjJr7LJnD9DGo7A+mNygupyv+3MkR78Ewtn36eD8/jNZNozRJfKsMNy3qIL5UJLRHHtfySybC5Oigiu1HXGkQrE8oD4a3vl/Df1xg+0Vh08GhrOMe8r5ENBwLxxWOMLjrqP+PvASOM9SZfMMlJ0pKecoc/+oo91uvU2ieqnOEMF/u34RPf1g389Ku86s1BxVl7gF9/j7/cEa5W3di3k5veEbY77TwqxrJvN7/8Fo/9OdT4upFO8fSS4O6SeDFXiReHEqO9xsds8ZTZzhGXa4mfOsub/cbHVZjiXNe51SeMM1tcvnnON9J4myzzVl+33D2KlJvmVPf6mit82EE7FRqhxGi5ClzqA+rtlavAFIv82TeydnTDQG+CaW0f3PSZQC5tw0zVpNMhBUmoyZ7+MhYtJB5n9Zowwbm7RzGdCXZtTU0820uVUVbKuYt5fiU7d4WodDhIpY/8dwyA7mbv44GCMi76IOfeEEQq3SIUmZAy7WgOdcNuv9BkZ4i8qrLuMUdCNKdvmjWVfPF/R2+ijER61KHdqGnkoZWhbvfpa5gzkbZO/vI8E3cHYly/h7pmHlgZPs7F8zhxErPGc9ZsKsuYNnYYB/PW+XzuPJ7eyx82Zfs5RrJ4Ir9d2+tA8aqZfOMiattDXS+RDuKWn1zOTY+Eet8whVFHTXw/3cTyg0woDPOpajp4vp7dvRafyQw3b2HpARaMDArQ+i42NLKpV9ZwyX7e+QR7jzKDM2zsb+pLVIlkCKsJ6suGfgfQTZKRCLkvISvcre7s3lf8pRQI/vaY6UxTLRIRsd0Kv3SjensG3DYut1/6cwhEo8F15dnHyM0LKczByKpmX+j5u/nbQdTS1dmXHLuR6AoRI8c1/TnWTB1aPOoXZnq5M1wtKiZHXnZuY1RcnrS0bZYba4Ym1caYYbOnNKv1mF8qM8YVPmy0SYqN9DufNUKVKrOMNkmJUe70/+Qq8BZfUWKURtVDH+BfAwX5fOSDXPtmdu8NZtXveRcPLuETn6GuPmzzqY+xfiPLn+/5TCZO4Dtf5yMfD8R3vHEcFtrxfC6/iQveF9KYmUwgua1Psvq+4JbSuC8oQpPZ0zWdZP6l/PPPe0y0B0M62VfIEssZmiz7I6eXQDuTCdniPn9DTpjrG48FHulM9Mz5jeeEdUYiGbzIo5HwWESQLWzYw6+zmqnGoa7nubEgUNnfwrvuZXeWFOLRMOa+rnftvpibzgqzof7p7hDpZVBVzI8u4+Nnsmw3G4ZXrHxJ4paHD3cR64MqM0VEbG3ZZGtLz1kWlaPKDA0OaFVvX3tImR53NPUTO2T0hJmdycPrc72/GEOlJ3JjoZVhbCkVxUHNWZAbRC0FceYcQZ/8D4Yqs+TIk5Gx0bI+LRL9UWm6uEEkakdCKjn8onVXmD52RPwV6n2xbJN/RlpSl4yUjMyhUydHXETUSOO9xses9BcJnYfs4zq0SkpIZyPmqBxpaWkpKV1Skr3uC/cSEf17ch686AJueAef/Ry33BZSmYvP4rtfZ/MWvvatsF0sNkAaMzLI/f84mHVO6LHrJr3mGu76RGibaD9CJ9Nwza87W/uaTOeXBLIcynj6ECIU9fIoSCeDTVtvjCnj2vMYUcxzW1i9g0sX8c5XUlXOI6tZu4trzub6izhhPAebWL6FSxZx2SmBBB9ZE2qCgyKVZl9LSF9ePI27NgayS6Q50O+Jp4xlzmj+8zE29iK3fS38fBU/vYJXTD7+xDcUomJe7yZxeb7p2j5qwgqT3Oi3HvAj9/rW8TqEw9F1hAtpKn10/UMjCnjlHF53MgvGU1lKYTy7RBqgHeH/ACK9VgGhVWHg961AqdNc+eIivn9g1NhuhLFmOcsJzso2/TcqNsp0pznBWfIUKjRCrgLVtpjq5F5pyr7vY5NqZMz1CmUqFBmhzm5pSSe6QL5iTWo0DzIM+K+OWIzXvTpEa7fdQXN2Bf/gwzy6lCtfw09+/n92BmE0FlxdCkeE3zNplnw3mFsPNdboSEbWvdHeEFowKqaF34tHB/uy/uQ1GOJ5QWDTjc4WGvsFMLtqeWZzKAO/sI32Lr72e+afXe6JFc3WbkxKpPjC7cyaEPFgW4XmtbVqmtI+dxvzJoWIcO9Qp2Uqw3ef44RRfPkCrl8Y+vF+v5ENB/vK/GeUhxrf+gF2uqU+ZO7mVhzuDjMIjuNSMSJPQbbxue8n2qTWg35svceP38sPhGOd2pg9hv9+XbbhPR7239zB3sZQP2zpDOqkrmTo+Zs4cshd/iOg2lYpCTlyTXeaUpXZi3QPCpW5zIec6II+RDkoMmlam8PYnn9Q1NnlYT823Wm6dOjU7qDdlrrZLGfab7NNltlng+XuMce5Nnhcg/2a1Fjmt1ISWtR6wm80q3Wfb5vvQgftcp9va1Ltz75pgUukJN3nm33GSg0fMSIFRAuRl9XTv5QFWpq8RiZPZtdumnspR5JJNm0J9bsRZRz861mT/TWRW8i4uT0E1lYffD2HM8tv1OS+Y3wGQ0dLUGBOO6PH5aVyZrA/Gw5KKsLEiG7U7w62bd3IZGhq5/m9OVriBRSn6UpqSGU8drAsGCrHYpTl25cbs29TO+9ZyJ4VdDapbenySHVxuBZ2DsNHeVUNb7yTy2dw9Rw+dBo3nMyPn+frz9CczcPm5YTApGuARVMiHUg0f/gL7GNOfFGxIdWD7Zr80TeHsa8cQWj/d7hCrCzhG1dz4ZxwvdhZzy+e5L617KwLBNiVCpFkNMIvrvs/Q3ybPWW/Tcaba4bTvd23PeaX6u0Vl6fKCc7wBnOd54AtSlUoM0Sqt7mRm94Z6nFDCVBOKuDs4vBF+H0je1/iVMpjhIyMDZbaYKkpTjbaJGkpK/3Fyn5Dfp9y+2HPX+cRBKOB7p93WWWXVX22O2CLv/jOURxhlPgM8l9B/pnkTCNWHgiwe9z20ZJfupGW65EZPHTJCjwGxT94mjMWJ7eXbq2zdXj9cbmFzFw8vNfIpFl7f/DqjOeH586/lPUPDq+JfdrLGTUxu68MW5bR0usYG1r5xu9pO3Eyr5gbToddddy7igvmsLsu5EJfvTAs7lfvCRaQ584KPQ23PMW5JzCjklufZcswas/Vbfx0Jbeu4+Sxgfz+/eXsbAppTELqMxph1AAiuRF5gfSq24Yd3Bwz4isywtneZK5zdGr1tLtF+rl9lKpwqX9TpEwGy9xuvaV9tonLM895TnKhSlMkJWyzwlK3OOgopjQeL1x2IufNCh/G3gbeeTMPrh84XRp/iRrmvzPU2uEun/cWX1FmjNNdZZFXSWgXFZOrUATrLXWzD7vSJ53i1UfeaSrJlrVH3qYbrdkV3nsrWdvxd0N8vXHQbs+485i2kLwkRAooeQdl7ydnshDxHcNzMlUcpNmbt7Jgfhh9VJeVoufkcMJM9u8PY5nSmZ7xRr2PoWpMsFL7B0Uq0bfRPZ4f2g+GwpwLmHnW8D+OTY+FqG/KqeE5i67kyZvDwNwjoXg057wz9A8Sao7P3t7Xsi2ZYmetoJrZXB2EJvnxQHL1bUHhnpvD2r2B1E6ooqaZu1bwtrNCv3NpQYjQKoqHR3zdaE2wdBctXaHud0pVD/G9cICGDs6bzG3reyK/aITFk8Ib8dTwGzOPyfIqLs8bfNLr/IcG+9Xa7VL/anq2/6gbSV2qbZPQ5VzXmmz+YfvKV+IC1xthrK1WqLfXJd7jrb4k78U4gBxPRCOcM6NHnfmXdTy8YfAaYW4suLD8DZCRsd8m2yy336Z+0XNGta22WW6v9X0ey8iosd02y+22ts9khoyMZW71XW/1jDvV2J5tqo7o0Gq7Fe7wX77jWts85wV/st3yrBdoVmSSGwm3oihVcQoHOBVLooyLh/97Y0snt9ZTO0gOKS8S9jkqdozO8BePZjU2WOqvbog9ICIUv5mRnwtRXiTn+CzEUiluv5NxVbzxDaE9oagoCF7OOZs7f09jU7Ce27GThQuYc0JQeU4Yz1veFDxY/0HR1U71lh69XNFIZp8/uOoyEmXG2bzuc8H7c7hoOhBqh11Zkh05kdf/dzCgHgxFI4PadFZ2zGUmzco/HMGguiNBS0fIXLV0BkKbWM78CcgE84+uVOhzqG8LyvXmjtD2VZhLa9fhYsH+KMnlnQsDmU0pY2wR08u5fHooHW3oVc9bW8tt63j1zJAKnVLGxBKumRP2sWRHIM1h4phEfBPMdaar3e3L/uTb0lKmWeQ/3NVnuzaNHvYzo01yhtcNuK9mB33fDTo0S0mKiDpotyt8QLmq7CTvvzFi0aDg7L54bK4JH/xgmDSSGRV97orHQq9LfjxkD4btcPAikdDhVz6SVRwms6ODAlKSbvNpOT4nLalDT10mI+1u/+2PviotdWhqQzfSkla533pLjTBWsZEiojq1aHRAq4ZD/XxL/Mwyt0pmlYngqnLmF1AWY2KcpjT/uZcNnSG98ooSbhhNfpSuDN+tYUnz0DwyPY+PjAnEl8F9Tfykls6jIaA2CQ+JGDxFnVEn40V0Qg+Wystkjl8ja3QUJe8iUtzTVJZpI7mL1F7SDaS7R0od5TFk2kjXsWQNX/827//XQH5dXUyayF8e5Ke/CK+dTPLTX4Zev1tvDmKY0tKg+ty58x/WcSedZPWfwxDa3IJQs7v4w6Fvb/WfQ4SVSYdIsHwiC14VIrBRk4OVWH5p3+b0I+HZ28NoobOuC68z+3xu+A2P/jB4kLbUZn3QS5l4Upj5N/eCoADNZNizhnu/eAQ16Au7wnWuW5wXi/LjxwLZdbd9JVPsOBgmDLR0ctuzQQmzbl8gvZYhas850VDXWziGpq4gUCnMCaR3+7qQ+uxGZ4rPPxEe+/iZvP9lIdAoy+Ppffz7w9QP36LwmBDfJCeKilljyaGoYbd19thwFHvL6NCi3DgjjJGnSIESMfGjk8UfD6TTYTXUjcqSEAUOFPHl5/D2MxnXd0k3dgTvuIANe1mQ4I6njt/hdhzhwtypdVDnjyM91o2EdjW2qTF4dT2p83ABxrg4ryrjX3ayo5Mbx4Tbv+4Kj31sbCC7J1u5uJRPVrGunf1HWEXmR/j4WPYn+K99VOTwxQns7uKeYUzF6Ie0vVpcP4wth1i1RCIsWsSrX0PVuIHJb9UqvvOt42NWnTOJ+PQe0kuspf4zdDwRSC+TMLg/1HCR6dnHN7/Lkkc5dVFoYF+zjqefPdTAnivq7Ee3WvH669SfsSBEhes3KnzmBV0nnyi2aasxihzUqTW7UBotX5EcO3qdyxFMVaJDSpukU1VYYq/k3zDKXnNfqMEtuCJEdOUTwjy/2m3B6Lp7OOzIicF6LBIN3pm/+xiX/kcYeTQcdLZw16dCO8OiK0N9ccIC3vj1UFdsrc8SX0kQtHSrRjOZMGfwNx848hijPkYf3ejf50xfsUlTdlE9nAk0hNTlu+5lfiUTSoO7SVNniO6ePxBSn72xv5X33x8a1U+sCCrPjXU8s+9FkR7HiPiKjZTQqb1XVJCS0KZBzDAM6Hqh3DhX+qiZTtOmSYcWpSqOYjrAcUQqw/JdvPHUcBF75RwWTAiWaN2r9gjGlPKus7khO5Os14o+Gg2p8UfWcuXpQ7xe/7TU/5V64cp2nmwhid818MXxlMdYVBj+zwhRYSITIripeUcmvqo48wr4ejW7E+G2pJkryvhj41Fc2zM4Bk4v807k579k1Gj27mHSZBoagoJ1wkTq6nj2meMY8RUR6e5aTtL4FVoPF9cMhGI5JiiWkJKSMUuZGh0qFcgTs069sQp1ZXsKR8uzMlFn13MreG6F0fK9TIWVKFamQI7nHZSTpmTNZoVrNllgpFXqXGKibQ9tsF+7TjGzlMkVlS+mSUKVQiXiMhiv0FoNTlOpSI7bbFUhX5G4iYo0S2iVVOuva1TeWsft/x4iq9nnZwUvBUHtOW5u321TSbYu43cfDSKTuRcxZZBpZAOhcV8YPluzNQyYLakMr1c65vCxRplMiO7WPcQ9n2X7c4YM7mNiTjJHrTq7jtCre9TIYFtjuA0XbQke2xVuLwHHhPg6tYrJEe81sy24VOS/KEVmRNQl3mORy/zQv9rsaZ3aneY1rvPVY3Goxw5/XM0Ni0MKc9YYfvV27l4ZCsKxKDMrg9LppPHBvPqPq7m675iWGWN41ams6jaNjUU5b2aYC1iSH/LlJfmhKWe63V4AACAASURBVL6yJGxTnMcnLwvK0aaOcGvuCOOK7l8ffh4UEVEzRC2UsV/K0wZr/I6YLqJE1AxpG6WzysKomaIWyNiXfX6hqEppG7OPz5JWjTYxp4mokvaCtE0O+6a1pnvIqCUVxnnkRRgZY2QOl5XKXk95pJmGIc6lwmxqpqXXdgeTnFwYan1/qwzaKy+moJC3XcsLL/C97/PnP3HXnZzxct7/gTCz73j1t6UbSLcQLSaTJDmwy85A6JR2onJ5YtaqN0mxucq1SoqKyBGRL6ZKoWodtmoyS5ld2UxBnqgxCpSJa5IwXannHZSSPhSxzVKmSZca7TZqdIIRanSYoVSemIfsNUKuxca61VZzjLBFk1ONVi5XrQ6dUtIy8kTNNkKtDnu19SG+/etZkxXXdrXRNsj1NicaztRUr/Nlz+qe57Y1kOkIpfuB1PX7N/CT63jZNSx6XWgf6PbpTCXpaApktfKPPPPb0FJA+H301PBz/TB1fM013PfpuLX3pJ16TcbMxZSOy8gvjEhJi3XG1dembH8hbfkdIRptawi9uPniEpKS0uLZ6aZdvfqtq4zxM9/woKVu9Jm/H6HWMcAxIb49NoiJm2yB3UJedoQxxphmn+HPzIjJMdE8+2221qO6sirB7lTq3xU2HuATv+d/rgxF3zlVzB6bXbVnG9fTmTBl4T/uCEqlS+cFIoMMa3bz2yeC0zlCWvSmS8OMwN5RXe+f8+NhRFHv6CAjpBcu/AbrB7fTiZgu14clLZHjElGTJPxqwC1zvRsxKU/JdaMun0NGrhslPSzHZSImSlkm1wd0+Gj28Q/o9D9iLhWzUMoKuT6ky1cPkeMhVOQEgUtHhoo4HWna0uxLsLOLm/YG4urGUMTVkApNrxU5bOsKn8GkXPZ06b3+GmeMU50k2ku636bdUs9q61UDPSaIRJg6lRXLWfJwILfW1vD5VVeHAbQLFvLOG3jqqVATO9ZIbKXzCWKvI5JL3qm0P2A4K4GEtFodyuVJSGuXUqdTUza9GxORJ2anFjU67NFqZK8FcFJGm6RicUXiKuQbq8BYhTqlFMtRo0O9Ll3SpilVq8NcI9TrckC7Op2iIv5itxJxu7WaqNhOLaYoERc1UbGR8pTLk5YxQZHNfeZL8sgPWPrj8HMGqQHWfBG86/ywfvxlVnCeSYcp7A9+I/t7hn8+m9REfrxk4Pet6QAPfovHfxYG1xaVh1pcsjOkIZurQ0+erBVwKh3m6q3LDqhIDbPmf5oZTklMk78s1zNPb9JcnmvfmD1OLZ2kKdJqTutU+2o6/Kp2mdaunp2eZ545xltph032ucKpcuW413I7slNwGjT6tTu8YO3/KdLjGBHfdi9YZ6kr/Ye4PK3qneJyhUr7bDfWDBUmqTBFnmJTLLDQxdo02mGlLu12WuU8b3OWaxywzSxnWOjiPqrCF4UDTUFxGY2EyKs3kmme3UFrZ5iS3n9G3v4mHtoQLl57+i0P0xl+tzwUd99+JqdPDbPzYtGQH9/bwMMbufkpNlUzupjfr6SqjH2Napt5aBVNvdPmqUyY53c0DjL1bbQd+aKZ40xpayXdIm22XB+WcAcDXuxjku6Vcr+oSaJOEZEvZZWk30hbJdcHJN0jo0nMfKRltMqoluMVunxT2moR5WLOOZz4Tirg6nI2dfLPo3isJZDXM23Up/hAJfc2khdlVA73NIS057Q8JucGYcy8AhpTQel5IMEDTby7glgtY+OcVcwn9/YJNt/oVT7n3/s01u+wxyu9xY7j0TLTXbfLZLJmzg1MntJz36aNXHwxhYXHh/gyrdR/npwZ5M4PQpfEetr+HEQpR0CBmAI5ljnggHYbNEr3ejMzAvl135eWsbNXHa5au7tt1yUtR9QqdRLSfmebdNbULSpyaPJKjqiktDXqpbL7TMnYo9VerSIiMjJiokbJM1mxpIwaHX5qo6S0zZr67LMb6eTQzeQZrN0TNBq9kUr0JaPJo4bxNc2E6K7jCANa8+K8/Rx++yT1rcO3LuvGy8xwr+WucKoRqWIFtfkaamu1G2m2OWo1K1eqQIHWXtfQZHYl2KrTItPMNk6dFpONPkR8LVp9cZj9olFRmey/wRDJ/hvOdhzbaTL9cUyIr12TX/oPr/URl3iPTm2ecbftXlCuSkZaRMQil1roYlE5dltrjGku9z4t6v3aJ9TY7i++r8gIr3SDpISdVvuFf3e2Nw17/EoELzPKeAUeXrJdwxPfCw/0y0tMbYuLfvg+W6KtWTfWft+KJRt5YsuAz0Ugqqe289xOygtDarKb+OrbaO7sicxqW0T/5VcykYhMZ0prii39pz93JPjoXYGkXywyhiENzZfJpn4yuoSPf7DaaVpPfatTRB7yOfT8zuzz05KWiHmFMPFviRBe5ehJo3ZggIamFW3MyOfSMjZ28p3q8NSaJB/ZHcjw/WMC2S1tCYeUFw01u6l5rGgTObVAzoW5Ep+sZW1nqO+9fRT/VhkiyC/t54meC3GeXIudJk9uH+LLldOndTs/xpTiYL6eFwtisz2tTCrmYPZjrchna/PhQ5b7IJNh4wbOOYeRoziwn9WreevbmDmLAwfCpIJU6viqGbuWU3Md5Z+i4EJG/4iOR2h/KJBgqoZ0qyDU6VF3doh4QERCJTIDLj/ThLAoVY2uPperDJLRtOkVjC1LO9DE1mo6enVbp3o9I5Elq3Q0rWpEWJPOHBO+fhv20p4d/VKUn5bObbc0vcP0sRnTkxlr9wRPcxhVmjF9TPhKbdwX9kO4NowdwZSsyHp3XZgdl86E1rNJoznQSPUAZFU1gqkVoTYfi/ZtGC8tYNbYcP+mA9RlT7nK0rCeriiloiS81o6D4bQozmfBJN5wesj+1DSH167PXuYKcsM+i/LYVsP+hsPLcrWaLDLNOOVW22mWcXLExERstFdCyi61GvtdOw9qVq3JYnM8Zp3d6qzLShIrjPI2VyvOto8t9bQHPHbY+xEXd5aXuci5qlRq026dTf7kIVvtOLRdsSIXOddipytXpl6j5632Jw+p6WW3N9pIl7vQ6VkD/Ket8Af399nmWOGYNbAfsMWPvFehUilJ7ZpFRUXEDtX5/uIHHvDjAZ6dkcheUOvs9TM3KlQqI5BqStJGyyQHERpEEBfNjlkNp0ajLu8xw6ZUs4asO2xEUJR12wCfYbTczqhdmiQPWQMHKoiL6kqlZdoHvxidMZH5Y/ntyrSmmhZqBlZPRiO8eSHnTUu6eQVLth7hjRyq9+UlIGW5XG+XcqKYM6VtMHC0Fwzncpwn46CoEyX8CMRdK2WemMXS1qFD2rPi3oyIhB8KlaHVYi5CXMwpEm45/EVqkqGFIScSWhZ6v9WbO/nE3pAKTQuPy/7/9epDBiPRSoqvpGFL9qJQn+Jr1aFWmNIzIyuLsSqcaPaQNmqLRgWyO9jJ7BGcPIqH9nJaBY/upyiHxWO5bzdrh/JJfOJxXn8VlZWB+B5+kH/9N+64MzR5z5nD975DS4uIHLlGy1EqboRWGyU0iIgpNF2uci02SQh2G3nGKDRNSpsWG6UHTdVm6Hqegx+h/OMUv4XC11D4KjKdIfLLdGQVnn0jumHlWtKNVF9Loq9UMBbl7efy2lPCRb2ilHuW89NHjtwBNLqE7749XOwjESaO4uktfO7usL47ZzbXvJyG1rTy4nCgn7o9EMSiKXz8NUF4XVIQyOZTtwdR4sIpfPb11DaHlqKGNj5xa2hLG1HE2xZz3lxue4pv/LnneOZN4EtvCtt3JUN9fklWbT+unP+8KrSvZYQa4cdvDQvbd1/InHGBuCOYNIqP/ZZlm7h4PlefwdzxvOeikLC55QkeWB2I9KbXhtGdbV2MLAp/+5Ob+75Pf/CcGcaaZLTdDtqrTo6Yg5o1azdTldZsBbQ3GrXZrtqj1qrVpFm7YvladSgSlytusole6xJ58gYkvje4wud8zCrr7LDbaCP9szc6qP4Q8eWI+Yj3uNZVnrRcnXpTTXSWl1ln0yFSG6vSt3zOfLMt85y0jA97tytc6L1uslf/KOGl4ZhalqUk+hjmhje750IeiGvoVE7//UBiEBFGFFcYb54yI8St1eQWO2zSrKbXc/LFvNlk0xQ7oMMvbBPF2SpMV6xWpx/bqkDMW001Uq61Gt1ul64BaiHRCNefxuvmsWIvzx5BLzBjFB9/Bbsb2VQ75J8/OGLZCcUvavJkD9Kel/QHcVdJq8mS2WAT2TtlZMRdJelBKSuQFjFK3BukVUv4iZDcOijpNkRl1Arx5E/FXSPuGkl/yQph+iFCThVF52VE82lbRudqomUUX4oMyT0ZnWuJTyaxg1Qd+QtI7ArX6IJT6NoUNBu9Dj7UDQfAXLOMUzngY72RSAfxQmmcqSXZodBJqtuZVUZxPPweG05wvmYN1/5TqOnBpk186AO86wbKy/nut/nOt0mn5aky3zc0WSUiLiJmg8+o8EqVLtRut/Guscl/y8g4wae12CgqV1KTtkHbSnIovIIRHyF3Yfg9EhEcXAp5qeYQqfpQP+yHGWN462I+8iue285JE/nqW3h8Y0gpDoZYNERXtz/FLcuCQcj/voO7n+O5bSFF+LJp/PP/snxbILH2RLj//Zfw4JpAruVF/OLdnD+PO54Jz2nr4sZfhd7rwryeoS0HGvnMHXw5P0RZ3YhEQjpywz5uujWU6W/5t+xjuPbskBr90M0hcvzSm7juHD59eyCw8mLe+r1Amp+/mteeyhObuDtbKRlfzkd/E16/u9ryyvnhvXvPT8N4n397Zbgt397Xa79VpxfsUKZIo1bV/eqaz9s+4Pu7R509erzKNuvRBrQ56PO+aaxKpzl5wOfniLnCRbba4a3ep1GTmJhRyrX0ii5Llbjchf7oATf6rISEXHEjlTuYff2IiOtc4zQne5v3eUzo7TrNyX7pW97lWv/pq71Ck5eOv6N5JkeHUnEXq/JFa73RZE0SuqQPS+CdYZSpinzTBleb5DLjREQ0SfiOTd5vltONMk2xJgm/st0HnGCLFk8NEGqnM/zsWZ7fy+YjROLxGNe/jAPNfPAP7DlCvn9ITD81dL+uefgod5CU9CdJ9xla2JCW8oCUJ/tsm3SvpD/3e35G0j39nl8v4fsGlVM+2cqmDpFckvuJlTLq/ez/IGVvDhaSHc9T8SkOfJSya2i8NVxfS6+m+S46VhOJU3oVbY/1tV4aDGc5RcEw+kFXHKSqkMYu/rAzXOCaE7SlqO8MTk4FsTCIeUgkk+zb1/N7JsMD9/PYo8GfsqPjUJoz2PxFbfd9GWkn+rJcFca6wjbf0WS1mT5qpMXqLZOjRIv16j0p4Qiy8KJXM/q7IUT+K7bDzKoKBLN6d+h3XrcntHvNHX9k4iMITJ7fEchgywEOtjCtMhAfIWX6/I6+E8UqS0P6sCA3RGkRwUpyWnat8+h6LlvIt68Lkef9q/rqxJKpvmpOQgp0zvhApO1d4fZCNpOXm8MZMwLBff6acN+ccexrCJEfIVI9kP1ottVwUtZhK5miK9Ez8+7Q+E+cPiOkYz+Z9fmYODJEpIW5Aw+ZeczaY14RO1KNLSVtk60WO92bvc4f3G+P/Q5k64PdaNdhm53OcYZXe6VHLFOrzr5eEVyxQpd4hWc87wnPZMdt8YwVlnrapc73TT9SZ5gjKIaBf3ji65TWJukclcrl2jZIs/Y0xTZoVq3TSg1eYYzNmq3T6IAOm7SYrNA0Re62xwEdtmkxVfGAxAeP7wi3IyLDHav5zjJ2DvS5RaKMn83YGTQeYMtzFJaRX0zVDNpb2PIMpaNDF2p1rxV95VTGzwnysC3PhhcbNYERVUE7vfnp4JRbVM60RYE0tzxLy0EmLWTkOGp3snO1vmOcM9JeyEZvAxHki1l5DbLt0vA5pSuJ5hMbSWwU0RLy51P3HTo3UHKFcCXovZKJhvsybXSsoPiS4R1JsUIvd8oRt+l+qWSGXdmFa1OvXF/rizBpGRKdA7NmUoOkFlG5MtJi8sTkS2hAWkKdXCN12GeLr6jyOlVea7P/0TqQs1F0JGUf7CG9TAapUNdL7gy1uUzr8FYOgyHTSrp+6M2y/w+beyODP68zcXhZNBIJKdQl69iUXWvc9Sybs9fZ9Xt5xw85b06orV1xMu/7RU9NbrBDiPTzp0j1+jkSCWR8f9ZW8u5nQ42wO5XbWyiTyQzPBjwaCbXJu5/tua+po6dW2R9/bc1lRsb3/FyxYh/yL97jOvd71C/d5nlrDkVn7Tp82v+4yft9xWccUOP37vNrd9pmhwyKFKk02gqrdPUT4Oywy7lerlTJ34b4Rikw2QhdUrapl5NVe+3XIiqiSol2CXW9agzFco1SaK+mQ0VrKFdgsjIxUbs1qdZy6IOrUKRTUrkCJXJtclCRXFOMsFOjWn1VaB1SqnUql+suu63NhvpxUTER8Wzr+37tTjRCrqiJCh3QISJigkJ5osbKt0qDUnETFHhe1Bj5NuvJTY4u4o0nBdecbiTT3LmG7f2+84VxFo4LdcApI8MJv/kg929iQ02vE3XWy1n8T6x7jLIxobN93nksvJg1S8K4mEgkdMEuvJg963lkOyKMmU5eIXPPobyK7c9z7Zd58naqZjF6Ek/exqs/QlMNDfspKAlEOXZGILtL38/dX2RfX8VlaHM4zl+nOKM+SNc6OtdSdH6vx7qJLuKQ1iISgqEwSecoMME4J5g2aH0vJuIUpSrkelKDRkmVcnXJaJUyQo6UjGTWjK1QVIe0Bkkj5CgQU63z8M7VM88Kzi2/+DlNQ4f8/VfaKe3a7VFmoZQOxWbb505R+TocsNU3TfchZU4emPhyphCf3UN66Voav0XrnaR2k27j8KM+Chy+j437QvR1QlWIzmaMYURhSBsOhZKs+GPNbiaPDi6B24bwPK5vYdP+EKU9tiFEVUX5PeRTWhBqf797hpW7+Om7mFDeQ3yxaHibopEeM6bOZNjnadP5w4oQdc0bH1K3XSme3RqO75mtIUotyA2EPBxxdjIdXrOwl193JhP2de3ZrNkTosXcnJA96i88/1tivxof9V9+4haXu8AbvNqrXOT9PumPHji03VobvcONFpjntS7xFld5tYtd74NWWX9osHLOAHSUI0cq+/ixxLCI7wwTfN5FqhRr0eV5+zXrVCrPu92jQNz3vMoyO32hVxH0Fab6hHO9yW22qhfB+ab5mMUqFYNWXb5umdutkcHHLBYRMdtos4zyI8+ZabQzTbRGtXe6uw+5logbI1+uqKmKzFXqLntcbZJK+V5vojxRS9VaoNwnzRMV8T2bzFFmoXI3mScuaplaqzR4lxlONlKzhGd6RXv5MeZUMrYkkN+UciaXs/rA4cR39hS+/7pQB9rbGE7aNy3ghtO57tZeNcH5F/D0XazsNbYmJ5dda1j66577anYEYutt9167k4LS0BVbOY0dK6nbw+O3MO0UFl4SyK+wjNs/S6KzZ/8Hd4X5JLn5lFUeRnx/jTVkJEK0AFFyZwVzkUyC9ucoewMdL5B3QjiUxE6KLwu1vtyZSJMzgaLziI+n6ALanjhy0LHQHBVGDfp4TMR8JcbItUmb2Yqdpky+qNVanKxETES1LlERrZJGiLtPrfON1CZtqzaP6HcQl1/OKy/hN78Z8j1JadfgOZmsGL/Bs5Ja7PAjk7zdaOdptlq9p+QaZap3i8qX0qrOEwPvtI9zS5rGb9DwRb3r78cLmw/wq8f5zOuDgnLCSO54NqQ8h0JXktecwpkzg9rysfWs3tXzWEvn4Wdpe4Kv3MunXheIqiMRUo7/eUdQW77hdC6YF4iuojSkYHdkv+IXnhjSoGfMCGRXXhTSoUvWhTTn/7yZH1wfXrvbnzmT4WeP8l9X85N3BTF3cR4/fDiIVNq6+kZ8nYm+UdueupCy/dKb2FMfRDVLN/CnF0I98gfXh+ixKI8/vxBe6+8JCUlrbLDWRre6x2/9wLWucp8lkr3Or3YdnvScp61wt/v81g9c4ZVWWa9Js612mmmaIoVas8FNnjyzzbTbXg1HSuOjVMzpSjRJelbLkMu4IYlvpAL/5UIJKW/xO406vN5cH3G2x+w41JtRqUhZv9pJgbgqJXKyy/dZRvmyiz1oqx9bLiXtbU72ORfaos5y+4xSaJ5K7/EH/+QkH3Kmmzzodmt82+XmqPC4nYdeY74yjbp82yYzFHuLKW6x069s9+tsYbdTWpe0r1qvXK4mCW1S9uvwqGpl4loktUk5qMvnrVEiri7bVNuN3U289/dhJZgT5bpT+PLlA79vz+3hHb8L0V1dexBBvHou330t1ywIj2fI2jn0E6tkMrQPERmUVfKqG3n8N4EUS0eH+ztbg846nRYa6WMhsuudEzrhLBZdxtJbwjCuv5EFWqaLg9+g4LRQ56v+ZBAHNv4mEFokSmfW/6Dh5nBfJkX1x+jaFtKiib0hLZoZotYWFbXYaeJHOOW7pG3Uaq8Om7W52Ci5IhokFIjaok2+mDxR+aL+qM4FRlmk1F6dntPkMhV9iS8SobSM/ftoHjra61Jru+8LKeJO23xPaBlpssF/iopLCYbSHfbY4P+JiEvrkBmMyFK1pJvCG5bppPNpfw3SI9TLfrIkXMzHl4fa14Z9w4tc2rr42p9CRNSVZNWuQGyEaG7N7jDpuz+e3RrSmbPHhUhp10G2ZRM3dz4TSLekIMyeW7e3x2Jyze6g9vxFLwHjvmx27YUdXP+/zBzb0+7Q3XW0u473/TzUAcsKwmPdEe33H+irXr3rOe7rVVdsbOeDN4d6ZCwSWjYIgpZP3Bb+hoqSIIzpfux4IyfrjFysSFRUnjzFiiQkswqKjFxxL3OyvfY7qE5aRpFCMTGt2g5lLcqUmG+ubXZq1CQqqlBw4m7LElyHTre40//4pGu8xp3ulZFxqQuc7mRf8C3NQ7SyTZevS9o4ucrlqB3i/B6S+OapNF+ld/q957J+bT+23FXmDf0O9sMVZssV8x1P251l8J9a7monusRMK4SzZYNay+01SakrzPKgrdp0adFlVD/12WYtzjfG+8wSE3Gn3ToG4ftOaft7WRgls2mr6n6K0VYprYPsI50Jt2Q6GIYPhoNtPNQv6/TAplDnm1oevszJNNY/zmlXhvpbsov1Sw8nKhEmzmXiiSFCm7IwEFZObiDOqpnZaZKZXs1FmcASB3eF+855C/X72LshpE0zKBnFiLFelEdkTA9RpjMvrtw3ABLbwq3vnbT8EVEKF2f/rIYgaOmNdCPJYfablylxqgVDtjEclPAyZXbo8LRGpyqzX5eDuowQ15W1xRot7lIV4iIeVe9Co7zSKGv615gzGTas56QFoUF9kLpev79swJ8zEocZOaR1Gsx27hCS22j7EyVvD8rLnOm4fxjHcWyQTAchy1BiloFQ1zpwdNjYFm6Dobpp4F68utagqBwI+xp6iK4/Mth5MNwGQlM7T20+/P49/YL/2gGGktc288i6w+9v72LF9oFf73ghKur93uUCZytTosoYV7rMSeZq0eprfuARy+TKdaMbzHWCWnXSUiqM1qDRD/zykECl3Ahf9inFitRpEBU1VoXlVrnTnw697l3+bIYpPua93uUtMjIqjPYbd7vZ7UM2s+/WabEy9ZIah5G2H5L4JimTkrG1l/S1UaddGoe8iPRGVMRJxhij2LddfshVIVdMhUITlR3aX6MOKWmdUloltElIZ3v0Yv1e84AO/22tfDFd0jr/ZoaMfRHBhDIWjWf6KMoLwm1MMVsO9ipwr3ko1NyqZnBwdyC99UsDofXeWTTGhsfDL9FYILH7f0DFFJbdRnNtqOMt+Vkgun0bgxlhexN3f4nX/HtIbe5cxdpHQjiVV8Qdnwsp08H+iFFxTihifjEnFFKZS2HWQLopyc4OVrWwspntHWEY6bFCmoafkTgGZirTTDLdpCG326jVQQkdUlqkbNUuimaprKYmnKVvMc4T6m3TrlnKfp3yxRwcqF3n7ru48CJe/wZ+fTNtR3ZLOebIdNDwOWJjKLyE0nfT9QKdz/hrRX4vFl3JIETpOA5GNv8/BkdGxhKP22AAFpexJZtFa9XmRp91kjnGqBQVscd+z1lpb6/WiN32eocbzTXTSOVSUrbZ6Tkr1fdKX7Zr99++7V4POslcERFrbfC8NTqGWtihXI5lmuwbpsPXkMSXI5q1Fuq98sxISMsdwj+zN0mF5vGYGm0etFWiFyvfa6M1ag6xel9LpL72NgNRbUJG4u/oCxyN8PoT+cT5IdWysYb9LYMYc6SSbHwi3LpR3y+nkcmI7FitckdUnc0S3VHFpifDrTd2Z6eYtzaEGzRWs3EZRSNCDRCW/+EIfwBmFHLNGF5dwcwiSmI98rZ+xyaR4UAXTzTwi30sqQuem8cAncMcyj4UXuYkI/pZ6A2ENGp6kdfBQb5IS7Ok150ZaJbSPNhKs6CQVSv52Md57WvZsoVEv/2uX8fPfnp8xhJBcjs114eor/QGxvyWtr/Q+TiJ7WGWXqY95J+PSt3Z7dxybAZL1rbwgV8ebh32fx1RUems01VERFp6WHZgxwoZGc9ZOaztttlpm0EWzVkkpay23mrrh9xnQsJzVg7r9fsjR8Slyu3Saalm7UMEQEMSX7VWcTEViqzLKhzzxIxWqCnLxJlsNBbvR4TjlBwyAk7J2K7BbKP9ygsODJCzjb6ICDLGgBFnRmboQDdCzpmkd5Hemf39ZSRX0isTetSYUMZnLqSxg3/6DRuzNqCjijhz8uDPKzBakdGa7BERVaBcWkqzPQpVGmm6BltFxZWZlK387JIZKsqNRNi/uYf0joTSGG8dx3snMj071eBINcBIJLirTMzn6jFcOpp7avjCNta2/vV11gMgR47FTjumo61Wv5jhs4sXB+eWRBczZoRbf5SWBtXncUGUvNOCOXV8OlLEplLyz5Rcl3Vs6cg6uHTP5nuRH1y6ieq3HObccrTIZAaX7h9rRERED8Xy3SLizEsiSOET2wAAIABJREFUm0icWBEipNqGrkHDJGOc5xRb7RGXo1C+5dY7xRxJKfd58qiO5/C/LyPdJ7z4x8duXdZrl5TpY4E3GIYkvjWq1Wj1Bida6YB2CWeaZK4KT2YNfTsk7dVkkSrjlarWYpIRXtXPGuqPNniT+d7mZD+2XKsu+XKMVWyfZs3DnH2WK+JjJps/gP/jCi2+ZKfEkf74GHn/hAydvyS5nPjFpLaG7/9LRVUJ40r5/bqg+OzG+FIqithaN/DzRpquyqLsLMNaJapExe3yuAlersU+M13hoI2mOt9ez2i2d2jiSyXY+twwDjyX/5rBm8aGyecvVvQSiVCaE56/oIQPb+T+g3+7cUCCN+dUE53sxCFS8xE5YnKO8RSQjIzUrb/lz38+8oYdHYcMquO5LDqLCVOp2c+YcTxwV3h7z72c/AKeeIAdm5g8k1FjmHUi1ftYcg8TpnHmRXR18MgfOViTR/lnKLhIEDz1fh9iRIoM6KX6YtDLuWVCFWMrWbU+lDTzslO/kyniOeHnjMOD3pwccmI905mSqfB7MhX6/HNywtt0tMiTa5Ry44wxUZUJqlSpNNIIRQrlZpufUlI6dGrRpl6DA2rtU22vagfUOKhei9bDbMB6Y+SryK0i3UnTUtqHDnjMNkWpIgXylCn2mOfliStTrMIID3j6kLn0YCiQr9JoU4w31USTjDfGaKVK5GZno3bq0qDJXgdss9MWO+20x0ENh2pzxxfdC9Bjd2GYLt+ZSjRJ2axD3UsVt+zW5Kue8Annmm+MBu1y5djda+hsl5Sfe97XXeYOb7RHs1EK7NJkbLZtAZ6y2xc95r3OcKW5GnQokSsm6ga/t8oBKelDjJ3pxd6h5TZ0c+SIWGyEC5QfdrxFYr4icmTiQ6aarnvIfVVY/EaO4XD3hvbgTz1/LBPLwqDhCaV8+JzgZT0QcuQbY4EOjcpN1aHBAS/IN9Jos6UlbfWARd5hu4fU22akGfZ4SvpYpJfG5vLN2by2Mnhn9kYmEzwya7s4mLUviWVJrjI3/N/buysaYV4R/zuHf1vPH2qPe+TXrRYbZYTxxpphirlmmmPG/0fefcfZVdf543/eNr1lMum9kYZ0pIOAIK4VxbWtvRdcda27lq8FXf26trXXVcRlFREVAUFqpENCICGk9zaTTKa3W87vj8+dZDL1TmbC7n5/rzzu42buPefM55w55/P6vNvrbaG5Fpo77P6T1PqWzx1OpR4vbLTVF1v/XWdr4YHK0jJe/DrWPMZlV7JjE6ecwwnPYc8Otq7nde/lO/8nEORJZ/H7n9PWQlklr38f991K1QT+/p386CtkY6l8IeTxRSrJZRey7ITwc2MTl18YfvXqtZx7RiCxA4388oYj5FdWyuuuDN+1ttHZxYqHOed0ntnMeWdSXsYDj7JqTeHjqVZpmRNc4EznOt1i801Wp0KZpNThpdBQ3qPwHjSPOnVp0mK3fdbb7DFPWWWNTbZp1HQUESYqgzBDbhREvdpGRVK226dUsVYdIqVatKvXOGQtW4liSy10iXNd7BxLLTLJRGVKDns5+p9f77llZLVqs9Mej3rSre7xoMft6xN6GiuSKlQ6UY3TlFskpVokp8cBrZ7R7HFtNsqNwd22X9rD2sxTPOLcH8Y0AnIi13rC0+qdYYYuGffb4TOep6TP7rfYYI9WzzVDUtwq+2xwwKmm2ZsnybScH3nMCtudaYZapQ7qtMZ+6zTIifzAo/lUFlba6xNu16RLJPJJd1jbTxLnWJFrIruZzm9Q/A8kFhq3yXnrIX7+OO89m1veGoiwuoS/beMvG45WfeiLhCIJRdrVi0uY62LdWmxws9nOt9zfq7dWSoVUPrE+Nh4WSnmC/7NgIOlFEa1Z/nqQ3+xndSsH0oEE40KSy6wSLp7Aa6ayvOLI/rG8+/Pri2lI89AouiyPgCIplSpMUWeOGRZbYLkTLDLXTNPUqVWuNK9RX5jVWq7M37l43MbYiwet9BXfH12XvxgtTax7gpqJbNtI7WSmz+WGn9DUyMUvZfKMsPnqh3gyL4U6ZxGJJI/cQ1kFZ19KRWVWc+e9ZMdf5f4wonZyTdIZVj/NoWYeW80bX8X0qcHymz+H+oOUlwbrraT4CPFNnxJumTtX8MJLQplCUSpYjokEp5/Elu3hGCMRX1zcPLO8xKVe7gonWaJa5eG4WaHo3TbkJwTh5mqV5pjhHKd5o6u0arXFTg9a6Xb3edRq+zQQRbLtQRsgKjBsu1+jm/3tqM86dPnTIALRBCWii5ztjV7pQmeZZOJRLs1Czi0lqVaNWjVOstTrvdx6m/3Gza73J9vsOnZ3r6Q6F5vvA2qdK6Wa/AjpdSdn9WjQ4A6bfUuzVY5lIk5iliLNsuPj6iQQ1kN2HXZt9s+sJMTwHrfncMlDL/7SLzsoK7JGvTUGl2B4xJE41B6t9vSxLG/Pq1KUjTVWk6H7F0QtyNH1I3puJRqnubkny1fu4b6twerLRazdz0M7mF8bSLC/Mn1Gl1V+kq/T6rHAC2xzj31WSevQYpeUUj3axMR1OiijU2asQckYXjOFf5g2kPS2dvGZTdzUQPsgLpCmDHu6eaSZ/9zHR+fy1ulBxJIwky0o5ZqFvO6pkAAzRpzrdB/2dvPNNtVkNaoOtxgazaT2rKO0lLlzqZkwuAu56RDr1h0uLYlyeZGV/GXPZoLHujhPGslkCBly5J2gURBPkExRXBKOk8n00HKNwsSyxoIww7d3MHsG82YFsiovZeO2kMw6I189k+y3XmtupaqS00+muDhYiueewZyZrFnP9p08s4mn++ss9EFMzBwzvNErvd7LzTd7VIuf0SAmJiGmRrXTVDvVcm/xKn9wu3f4uEN/6ZScQLzYuLv6ExLOcooPebvLXahS+bicY0xMqRInW+ZES7zelb7rl/7THzQZnchwXJE53mmxTyk6LAofyek+nIURlxKTUmyqmd5ggnOt9U/2udloL9pkRVplbdQ1Phbf/7MoJvXCIy7OqIncKNPmixP52MUgf6OONHduCq++eGroBul5EgtEttfj+UKO4HrLSevOuzQjOV391UH6o7w8+Iba2odPn59bygfnUNZvMbGnm/ev4y8FxOgibO/ikxtDOcPVs4KKM2GSv6CGt0znq9vGPAkstdCVrhjXRJXjjgm1fOHjvPRlIUCXyN84osBSXV38/kY++AHSabkcba2hrLOjne6u0Gfu4bu56m3hu7072beTro6jia9+L9s28Pr3h1+18m90tDI+kmSFYfM27rgvCDA/9Dj1B0il2LOPjVuDKl8sRkcfM7jhIL/5Y7DoJk7gT3cwd2aw7nbvY8duJtXSNMTitEypK13hI97pRIvHPVY7EmJiShR7JrVJdmqXVCWpyaTqQnJL13CtyEaBGlXe6XU+4C2mm3LcSD0pYZlFvu7TLnO+z/mmJz1ToPUXM80rLfE5KRP0aHDA3Q5aocM2mfziPWWCCktMcrEJzlZugRN9Q5e9mjw6qjHv1m25MvOxtQBj4JiJr0mXEskx+4GLi6mqCeK/HYX1mR07UpR+EFFwcWY3Eqsg/aAhuyalElQUBUKLIiZXcMXioMqyZ/y8eIfR6hiqfQkzyvPO51Uvp6SEzk5+/Vvuf3iQbYWShSXlR1shPTm+sYPbR5mY0prlK9s4pTK4P3uPmYrz5uncsJ9No3L6/b+Byy7n1a/ldzeEjgzvfR+PPMITq8J3s+fww+8f9vt1tPJfPwzve3YEYovF6Opk09pg9W1dT083K27r110gzW9+yLzF4XDbNoxOn2A8kMmyrk+R+IYCJ/2de8I6LRajpZUn+xR1794bXoNhhqn+2fu8wStUjJP1cyzY74Cb3SmKRypOI33AYUH18cBsM1zjo67ydwMaKR8P9JL5y7zACeb7hH91q3tGTIApMcMiH5UyQbNV1vqIRg/kxRYGYqvvmu4VlrpGmXkW+JBV3jqqmF+NpEZpZXlVpUEUc4/CMRFfVuQL7hETO0p8+lgwbSZvfhdbN/GLH43pUAUjVow4nV+n5E10/YCSdxOfmNfrHQQn1PHtl4S+bD1Z5k4IZQvf+NvQWZp9UVPKidN4aNvwDTjHjJpqXnwF3/4h+/YzZxb/8GpWrwmzSl9MSvHKKQOTWZ5s47q9x2Yk1PfwnR2cVR1ih71YUMZLJgVC/f8VYpx+Rqjj+8THaGvjRS8OpPeL/wiW3ne+x4tfGjqz53JyOQ7lQ9nd/Z79zf0UPtoG8UB1dYb44P9GHGrmniEkRwdDr1VyqfOfdSuvLyKR+z3m6fQm3ds5cCDMJbFiEqVjP/4i8/y7z3u+8ySe5fOMi1lmkR/6so/7suv96Sgdzv6oc6FKy6U1etrHHTB8G7WMZjv9UkqtZb6szvOUW6hV4VlMaZFScVnGHuOLxVi0mHkL2bGNxoNhRZlMsWdvqylTmZjl0EEWL2PufDauZ/MQq8xUESefRm0dT61i7262bea+O1l+0pHtyis4/bkhSL/yEZrHrxsFQslCbq+wEssE6y8+PV/CNAT2t3LLehbVUZLk9o1BkuzeLaFp6Ug4d35QdS9EsX0w9DYpGOrnw0ilQqX8/vrQCWBfvp4iMcjDckoVS/qlmeYibtw/tnjcvU1BzeXs6iOfJQTi++luWsbmduutGy0Uw2Xu9T1meB9nxCIqKti3L6QrRjk6O5iYF8tuaeHOO/mHN/DtbwZifFYRD51/E5OJ1wVB6143cmY76UG0tP6H4CRLfN+XnO3UY3Z9995L2XzOeCTKx+/ih+ODhVhW3Xrc4JbDKiOli8i2UvN8Dv2FzMgdm4bEPLN81xdc6ryCzzOIjKQ1a9OkWat2PdLi+ThelUo1qlQoE++TcDIUYmKmmexrPiUSud4fhyjniKnxXDEpzVZp9GCB483a54/m+4AS01RaMiriOyBtnU41krrGSnzTZ/KeD3HvnbzzAzy5kvp9IT5/3c+55Iqgu7tjK1e+mpWP8s6r+cE32TKI4s3lf8eS5Wzfyvv/ia9+PpBaX5JMJHj9W0ItTxRx2pl86yvjLGiRofsn+X5u15K6kOyfiPrU3JVIOdNsG9Tbr9WBDv5tRVgMxDA5qsynlozsujtpOmfMDoK0U6sGd43OqWVmDfdv4YTJVJVQkmLZ1GBhVhXzwFY2NfCqU4Nw7TP7+ONT/SzIg408s4EvfzZMtJUV3HVf8B31x/k1R1tl0Jzh7kNjY4BD6aDeclbVEXdnLMayCuaVsvrYJ/eHPeEjrhmVm2eKOu/1BpV9SmsGDFmz77l2XHt+wd6oXtf+s1lyDqUlgdh27AhWYHFxSHdMJakoD4uWZwuxEopOp/xKSs4jOSuIWOszhpbv0fiRIfav7NNxPRuEU5/FkuiF5vh3n3eO00Z1L+TktGiz3S7rbbHBVjvs1qhJh04ZWUkJFcpNUmuOGRaYa4HZZphmgqpBE2bW22KFfGptjIp8k/tsC2VL6N52bOdZZ4Kv+KRLCiC9KK9gtck2d3vQfR62wRYHHNKhU1ZWTExKSqVy00x2smUuda5znG6KumF/R2hGMNG/+qRGTW5z74BQV0xcialiYjpslx1FeVCPA3ocUGqW4sMJMYVhvhKnKpcWWa9T/QglXsMS35JlwdK76TfhmaypzReS5p+N3oLT8y+muoa6SYEUFy4eSHyJJM9/IQ31YZvZc5kybaA1V1XNhZfw0P3h52XPoaIyJL2NB5IXkttEbCLZ9fl6vhsGbpeRNUMN2K9VmSJ1ytVHrXIi01TbWcAkOaGUt53LDauYVxf+f81tAy2/uRM5d14gvqVTmDWByZWB6F64jJvXcvkSntnPI9sDj/zj83hsBzv6Xptsll/+J/c9ELIE9jewY9dAE7wkzqmVAzMMd3ezZYxxuAgPN4eyh+I+x69NhpKHMRDfGuutsX5U+yy10JtcNSzxtWj1U9fbZhyEQfvjsQyvexVz5rJ2Dfffz5vfwr98mm1becc72bbt2dPwTC6k5iOUvzL494cSKogNRcQxKt9G5ZvC/3MHaHgPmSHUn8cZtWpc42POd2ZBpNdr/ay10Z/81R1W2GCLQ5oLkjosklKl0izTnGKZC53lTCebZ5ZSJSKRP7srlDGEX6hjHcVzaFsV6vmOBUWKfNDbvczlEiOQXk7OOpv8wHX+6Ha77Ru2wL7BQVvscL/H/Mx/WWaRN7nKa71Undohr2tMzAxTfMUn7bDHWgPTbEPWZpQvtRrSNzUI4mL58xxt7siWfDZn83DSgX0wvKszfkRfMhvORRQdacdRWhYsvnicrZt5/BEefziQ5YBjCfuuW8PmjTy4gp2DbRejqztYl01N3P7nkMU2XkidHYL+RVfQ9f0gUYi8lMSR7TJyGvOyasWS3pSXvOrQ4warnG2enC0OjCBdNbWa50wPvcNKUkF8NxkPVtyQyF/f7kwguhOns34/C+tCAfxFiyhKUFcRml4eheJiLjovWHkbN4fVyfPO5/6HwoXtRWWC2YM8kTu6gtU3VmztpDUTZDt6kYyxbIwKIf8b8dBDfPEL7M+n9D74AL//Pe96d7g+W7fxve8W2LlhjCg+i4nfovjMMRS0R6TXBPmzeGUoVCu7gpaxE19ITBjakZ2U8C6v9zKXF+T26yWE77nWTf5ir/pRT6o90g5odECjVda61o0mq3OaE73IJU6xzE3+clSBeccztD8VyK9rkN7AheByF3i31x9WXBkK3Xr8xs2+6N9ttHXU59el20prrLHeLe7yBR9xuucMeX1jYpY7wWd90Dt8XHOfkrNIVqedIlTki9XTI2Wg51FqpmLT5PToGkVyX7WEqYqkRRYo0S47Nq3ODet48ZVc8ZJgha1bw949XPR8Lvs7zjiLm2/kgfv4+39g+ozwLO3cPvBYmQwr7mLZSYHIoohNG1i0JMT95swLx1vzJE8+zoknh3hhRzsbC5D7KRSZtZS8l8QS4rOOSJTl9tD5TQbjsSollpvuEdt0yeiQtlvTAG3SwdCZZlsjP/hb6M6ciwYnvWwudI2Ox5hUEdoWRcL2vRJP8PKTwjGvfyy4QY9CTHCZXXguDz0aiK68jMsvYdWTRxNfdYraQR6o3V0hq3OsaMyE7g11fYgvFmNOaYj3DbgGpWLiohH6bv2vRGMjv/rlkZ/b2/n0v/Bf1wdX9MZN7BjkoRlvpJZR912KTju63TfIBAKLFYUejiOh+0nSmyg+FQnKXkDrT4LQ9TEiKeYq0/zeviHbgp3pZO/zxhHJIBLl425/9kX/bsMxEMJQyMjaY7899rvNPWrVHNVpQIwJl4cYX8VpNP+NllE2kJ1qko97j9q812kodOn2Pb/0Bd8eda1df/RI+4v77LDHd3ze85wzJPnFxb3YpV7rZX7ouqOubaMH5XSpcrIpXmyX64yUHp5Qara3KFanwzYto4jvzVRkqiIdcmryjuiRMCzx7dzOT77LghN4Zm2w+lavDFnyEybyna+xbw8HGkJq9eJlIdFlKD29P9/E3r3MnBWswmwGEaseY/XjwbrMZPjZDzjrvOA+3bVjfNOx07eRfYqS99Hzm6DggtDSrI+nqVaZxabokbFBvcfsCOVqGk1UZpFJapVbb7/WYdpm7D7E4zt4zwWhZ9djO/jrIJ66rQeYeiYfvoT5dTy0NQhbd2do7AjvhzrY3sjFJ1BelDdS+84P1dW8682B6CZPCn+wVIptOwZmdFYmKB3kpj6YHp9QTWeWtkEmr4mpUN6QPfIgxExQ6o0yntTjXv+t4p7PFtrbefihkbcbL8QqmPApik4NpBdFgaS6H6PrbnqeDkrKtf9K0ZKRj5c7SPdKik4Jx0stJzHV/Mx+55qgVpHf2uMsExSJW6VZJHKOWnt02abD2SZIirnTAVMVW6rScpXudtDlJkmJ+6sGjXlXTLkyH/S2EevXIpFOXb7t5/7V946ySMYbGVn1+iniRIhTfhJNd5MYQqZwKMTEvNbLPNcpw55nVtav3eTzvjWu57jOJlf7rF/6htOG0bgtUexqb3aHFTY7snBr9IAmj6t1nmWukVRur5v0aNC/hUBMkTJzzfVOc7wN7PE7nSN0fTh6vJ226vYcZbbqdqgA9/WwxBdFrFkdXle8hGkzQk3R3+4ZuG3vdsOhp4eH+qnvbFwfXn3RlubOETR9C0VKQq1KrTp16A5x+B10/5TsNkPW7bXr8TshJ/yQDr/2qFrlWnTJiVzvcTmRrhEucjrHj+4PySupBDuHsPp3N/PZP4cODvtaAtHFY6F8YkdjqB/c0Risvaf3Bd74+YO09x1/cws/+1UIqP72pnDBs1nqGw4LIB9GSTz4XAec+DgVOqejwXvzlcXFEhMVu0SkU497FXuhlGXS7teX9BJmK3JRXrH1oIxNUs6W0yDtfkUuFVMibXU+aH+GrF16rPCsFWxPKA5EXj+CpZNIsGAhZ5wR6vaSiZDp+cQTrHlqbOrLhaDkAspeFFwyUUR2J4c+R/vvyeVvylhV6LJQELL09MpLxUhMIjnXqZkubbKmi0uLnKrKj+1wQI8TlMuJXG6S2zUolbA1T4DzlPm9fRYqd5YJzlCtW06DbnfnieUCz/UCF40Y10vL+L5f+aJ/H3ft1UJx6FYSVeTaQyLvaDDTNG/2KqlhpudIZKU14056vXjaRp/yNb/0DZPUDrpNTMxi873RK33ONw+7ens02OBLTvUTJWY60TfM9W7NVmm3WUaLmIQidSosVu00ZWYj5oB7bPUd0SjazOVwglLlEqZL2KnbwbGKVPdi5SNBAul/E5ISrnKeSapk5PzavZryrrTsMNJH0C1jj6PTL/f1cSXsVnjVejobklRGwv7W8OqP5uyR4zDMsaIolDF8/yccPHTEVI4PQnAxBvViDCUkOlpE0eCGWzwmHquRMFfaw/mlwxPiqqQ9dfSmpoopF1cqUidpmW5/kXKOnBOknKbTz+U0KvU23f6k2BUyNsmNJUllQjGXzGBWObfupLqIE6p5qjGQ3KLq4IO+azevWcjEEn67mUPdXDCN1jR37qY7/wcrLg4JLP/4wZAB1p33eZeUBMK74bd8/v+E4q/jgiTlV4VMTIJW38FP0P5fxmRdpzcHKzFWGrJEk3Ptt9ql6tzjoB45TTL259tDX2aSzToOOxT26HJIWq2UnEitlKSYLlkH9HhCi7X5Sb1EsTe7StUwCUoEQrjNvf7Vd//bSI/g4pxweXgMDv6etscK3/fFLrHUgmEJvl2H/+uHth+r0EUBuMv9rvN7H/CWYV2er/ESv3CDLX2stAa3e8qHLPVF5RaodrJqJw+SBRrLa3am1fuLtT6m085Rj7Ve2onKtOQbSI+Egomvfv/I24wWKTHlEkrzqm05dMlpl83bVWNDmWLlSvzUHS53qslqDhPfWMddJq5U4rC+e0akU06HrJ5nMa17ACJB+r4XRUW8+AXcfFvQkOpFNhqc5FIjJwwUhHhsYGE8ZCK53AFpDyhysYxNIl0iXQxwGUdyDonpFFMqyqsjxhRxOIdrn6AR2SNugnBLjzE5Z0EVFalwDsVxXruQTc28aA5729nTwdRSZpSzuSW8NjbztiVUFVFTzPqm8Dlc9Dw++S/cfRfX/YpdO4Nff2IdV7wwZHjW7+dL1wzRrXiMSEwMvfh6XZydd9PxB2N2KWcb8jG9UsTFktPMVWqnTmeqsUunux2QFRpn3abBdMX+YJ+dOiXEdMhqkrZSswXK3areas1aZZRJ5CkzFKo/z9kjWnu77PNF33agwISK44IYJXNDZme2JXRqKBTVKl3lRZIjWHsrPOo29459rMOgR9pPXO/lXmCumYNuExMz32x/52Lf8Ys+Y8za4wat1pnj7Sa7XKmZEkqEIH8kkpHWqtU6u/zaHr/R099tXCDiQow4ebjucvg5+FnX6iyX8BzlLlLjTJVmK1GdbxCSQ5us/Xo8pc29mjyiVX0/KskprDq/XZcGTd7r7zRoscuxrahjggjqKSqcrcpJKsxQrFpCUb59bo9Ii4w9uq3R7gEtntBqn55nL2J18omhfGHu7KDgQpDAP/8cbr3DUWmrHblQbtAfVeOkClEUD+7U/mjPkk2IqdDjbjn1eXflw8L6cbLe2zKnVc5KMUkk5ByQcoq0R2RsFGrOMsjqcpOUk/PHHOMqraEzWHiP1rOvk5aeQGL7Olhcw572IMQdiwXrbkZ5UDVoz4TY5oP7w7a9uPRStmzhQ/9IQz9z/cEHgoD18y/jW9+k9TjEoxKTSfZmQuXo/GsoYh0rog6iPi70eHBPxsU0STskbXMfq2ut1sMWXF/0drzvu+0KR8shXeF5Jpk47HCycn7lRitHkRhxXBDR+gjxMmouo/Hmwnc90WKnWj4swfdIu9aNWkfTDPkYsd4Wt7jLe7xhyDElJV3pCr/wu35jymn1lLU+bJOpyi1UaoakKqErw0EdtumwRXoM9bNVEs5QoVFGpYRycV1j7cA+XigR93wTvMt056pWIylm6D5Yl5ngvWZYp8O19vlP++3PT9xZ0ZBZX30RF7PGDndYrVt62LqWwfdnkVKvNcXL1Fmk7HBniKFugkjkpep0ytms0x8dcJ391us4vgQYizF7Vnh/+xvZuj3E94pSIdOzP1oyg8fzJhWFEx/rYMsTVAxCogfSovQB3W49/FGkR0YLiiQsFMs3Rc3ZKePolN5uR4K/GasO/z9nj+5+nUGOGXMq2d/JrArmd3LdRpbUhJjlygM0dtORCYSYzlGZYmIxf9zGWVOCpdhXVSCVClbeoUGskHSa9c9wxpmDu6THA/Hq4I4kyBOlx0kxWVbfGyWSdKN9eUtt/Dwflco93/kjWnu77XWdm56lZqrDo+Pp8N6+enhFqP642DmqDW8ibrHDCoNo7x4HZGTc5HZv8Iph62BPttQS8z3qyQHfRbK67B5VicJokBTTlPfy7NEz9jq+8cJMxT5ptteZqrqANiG935dKOFWFEy3wUnU+Y6sHNMuKDp/ocCgzlOnaAAAgAElEQVRR5FxL/ZcVo05lrpH0JlO91wwLlOa1Zgvvc1Um4UTlliv3apN9127/YV9B4z4mRBF/ujXEjW64iTvuCcRXUhy0OvtXzDdngq7mvH5CgjNKgqXWMUbmq0tR3e/2iqJQ3zfEoWOJHtnE3+R6EMu3dOkilqSolmwnmbzBEC8Kn6cq6Tkk7DNeiKKQTRSPhbjdhubw6ouDfZJRbumTgfaXQeIT99/P1R9gylR294s9lpUF0nv04eMoV9ZbSAzZIzU8Y0b+j9CLqENWpHWc7/G5Zlpq4YiZnHd5wAZbx/V3D4lYjJMXh7qjVeuOfr6mTGT5Ah5bK2rJh1aKUiybz7IFlJVwoIknN7DlyP1QptT5njvieT5opb3j1Je0EKz2tM22O8XyIbeZoNp5zhiU+I43GmUckNEjZ7vu/xltiRYr822LXGpCQfUV/RETUyTmeWpca6mP2OwmDQ5KH9bVGwppGVNU+6CXatbuRg9pKCApZYFS15jnZSYpPuwzHj1691yg1JctcIYq/2KL7WPtoTccurq4Iy8KG4+Hjp7X3ziwOLoty4Z2nttHVoxQ1D4xRccYi6kXlQ2UQ0tHrBs6xloxn6mXsunHlE5j1ivZ/BPmvp6ymSSK2fwftG1lSb67RrKMbdfTMp6SkvfvY/VBMlGw7MaKBx7gVX/P93/Ajb9jV15JZ9IkLn8Bp5/O177GBRce4af2dlatOtKpdSyIOo90Q40l89Jk44DkFGK93oSI7DA9t8aA51hiognDbtOtx5/dJT2CVNX4IeKK83jF83nZ1ezNh1FiMd72Cl52MS/7AC3tVJbxibfz6itoag39mCZUc+Nf+dz3DpPmNJMtNn/EEoYVHn5WrdqDmjxujZMtG3JscXHnOdP3XKtHGnGlZqiwVJnZUiaISRU0l2Z12eXXug3RjmMQdMm5QJVF0u7SPLYC9rFirhLfc4LnqclHwoZGlLfJhhIUDk0mS3zLIj1yDhRwg3dL+7k7lSkWiQpKbFmu3Pec4HzVYx5z37GXiHmNyeokvdcGW44X+RUX8+63hq4MMSF+9NTT/PDnRwueZiIebOZ10xxVhz+tOLQp2jkG4ovjvJqByS0H0zw9tFXTuY+qxZRMZdL5pFsom8Wkc9nwfSadw6wreeabVC8N701riMZ7DsihZRwn0Be9iAsvDFp9F1+SJ7MoaP8lk6HU5N++frSrc/0zvOyloUi2AMRCvsDgqfPZg/muyzVIkVqKP47xpGIUP7ePC7UrFLSPM2I4zYkjdl7Yq/7Zje1FuP0B3vUqzj6Z398ZPq+t4kUXcs+j1OfjlFddzltezud/wO/uoKuHmsqQbNbHUpxn1pClA71o0TZqyb6xIivrUau92VXDdoZYYqEJqjVoN8+7zfEOpebkE1oKNx4ymh10X8HEF8c23fY4oK4gWZHjSHxVEj5vnouGIL1IpEdki06rtNmgQ5OMOCZKWaDUicrNV6o0rx4eEzNdka9a4FGtR5HOYEhJeqVznGCGdl2+7zb7hsn2mq/EdywakvRyIgekPaPDOu126tYqK44JkuYptVy5RUpVDOLSTYi5TK1/s9C7rB9RSPWYkE5zy+2UleY7oM/jOcsHjx890ERDD1OLj3xWHueKOu5sPPY439RiLujXaTyKQseG7UMTfqY1ENmUC5lwCht/TPHE4OasPT1M6o0rj2zbuXd08ZPjhXiKicvD+A6tD+8TT+TQM2Q6cd+9vO+9oztoa0t4FYDJp3Pq+9l6CxtuMDChLVsf4nrJUCsVVFZ+2Ee94RiQmEzZSx1+ArN7SY//hFykyJIR3Jyw0dYjWpmFoLIsuCQTCRoOcajlaKWMqvIgJ3ew6Wg3ZmU5JUUcbGbdVp5Yz8sv4c/5zrunLWPGZP54T8jQLS4K3z/+NL/4I535+7914CJ8sflKDV8zVu+A3WNN3joGrLdZpy4VhpYcnGaS6aZIWGqJz0moEEnrcVBWu5y8YskIyGgbVS++iVJOUGK+ElUSbtSobagC7TyOC/HF8DpTXGXSoO7NnMh6Hb5tlz87aK8emX4XJCWmTsp5qr3bdBeoyWdQxixWZqGyEdcQpYpFeNI2XdIqhrmpqiV9wXwXDELUkchuPa6zz2812KhD2yCpMkkxEySdqco7TXO5WiX9Wn7ExbzIRB82y2dsHf/Sh1yOTVuOkM7W7Zx1Rkhwaex3M2zoCOR35eSjuyi8ZBI/2MXGY8z8e+FETugnV5GJ+FPD4GoufVC/gpM+S+sW2reFuF7HTg4+RKaD7vwi+r+xYGQA5r+Y874Y5s09D9C4jlnP4y9vzRPfhg3hdRyQLOf0D1M9j4e/ZPALE7WFTM6SC4JpWHwWFa+l5UeOqdA/Vkzluyk+7UiJRNd9ZMY/eaFSuZn6a/MdjUhkrY06C5ks43EufS4ffj3zZ4TxNxzih7/j+tuPlPxc/RrOO5k3fibE43rxnqvC/m/8NPsbg6X3L+9g7gw27+SlF7N+W4jfQWkx0ydz72NHSG8QxMQsNHdEgt+rfkwF65MktMv1qaYsDHvyv3c44qtQbraZIi+QUKHbXutdo9H9MprzxFcIcrpHsYhpkNYl52kdyiU0j1W5pRcJzEZNjH0R+ww/8cxX4mozlQxS9JgTuVeTD9roKe1DHictslePGzS4V5N/Msv7zVSet6QKGXibTiusNdUEy8x2cIgbJo43mepKdQOIOifyoBafsNlDWgYQdF9kRBqk3eKg+zV7r+k+bo7qfqNNinmb6f7qkL+Od71RcXGQLZs9K/xcUc6efQMly6Azxy/2ctlEKvuMcX4p753JJzfRNUqzb0Ep75tFUb8HeFMHfx65nKRtK7v+FGJ2UYbOPWz5BVMvCy7NXX8IySy7bz6S6PLfiVicuhN5+BpatrP8LZxwFU/+iK5noZRs8ikki7nrfTQNqREdBYWWyreSnBfckzWfCRe07T+JRnEh43VUvZuaDx1pS5RrpPWXHAcPRrWqEfUqI5ENthSWwHb2c/jOx3jwKb5xXWgX/4pL+PLVtHVy411hu8m1zJ0eFHb6YlIN86YHFzXBpfnRNwcy7OriojP4/n8dseii6Eh29TDlZUkJM00riPi6R7BmhsMb1Vih3SOjDLU0a3FIsxnDLEJSUmaZYWfewGhwl+1+NCoVlmPFNEVeaoJWWf9ZQNnaiPyRwOvjnBOjE2X4bY47h/gDxvAaUyxWNuCPGIk8pd3VNlo7ikLyBmlfsE1K3NVmSI2iGeNkNeaYrEn7kLfUYmXeb8YAoo5EHtHiHZ6xbpQqEM0yvm6XInGfNEdxn2PHxEyUdLWZHtJSkNJAwUinuftvTJkUVrfd3WzcMlCyrBd3NXLbQa7qY/UlYrxlBus7+Nnuwev9BsOUIr64kJP6tTvqyfGzPWwfWcA4SrP9+r4fcPARDq5OcN58Tqmge5u9tx1fVkmUkCylp5miKkonkcvQsS9vxfUOL8cT3w2JN6kKVn6DbHcgwQEugRjF1ZTWkcvSsT9YsceKWIL2PTzyFToagss1NxT3pNfT8n1qvxBUVhJTmPh1yl5M+2/pfjzE6Y4SqE6E5JVYKclpFJ8TLMWSc5AnvShD23V0F9ZwdLSoVqnc8GKXPdJ2FRIPSiV51ytCgsnHv83+fLH0U5s4cxlveSm33k/nKOLbu+u5+xFedBHtXYEo/9pHg7W9iw3bOW1paBN2YPD7tkiRuhESeCKR/Q4OmtiSxPnKLFGsXsat2uRwcT61ZKUuK3WpEHeOMqco8Xj+symSZklapEgaf9SqTMxlKpSLu0e7AzpHFMGOYbo6B91ruqsUmyyhXGYUKlfHijg26TJBMl8oNzxGJL6JWB7jn7M0YwHelGBFdnCZy6mKvMrkQV2cbbK+ZLunj0E9pV3O1+xwjipnqyooO6hCqZPMdbtV0jLaBlnlJPBmUy1QOuCY9dL+xdZRk14vuuV8z24XqfE8NUcdPybmIjXOUuXO8bT6Skt5xUtCAXuvCsjGTfz02pDh2R9tWb6yldMqg6XXS1hVCa5ZyNQifrybPd1Dm/mpGCdX8ql5/F1dIM5eRBH3HOIXe8ZWG7hkamhQuOVAaEz4Hw/368A7vpjzfJ7zDtb/huVvomIG8ST7V/LAp2nK53EUVQdX4+zLKMrzfXcLa3/Gmp8fKbOIF7H478MxSycJhL6Ox77KvkeN2ndbXMMp72fhy0Nmay7Nrnt49Ku0DepxzIbuCaklVL4x9NqLlQfiK3tBSIDJHSQ598guZS+iaCnxWhLTSNQi2aezQ47OO2n6v0G67DigWqXiXpIdAj16CmsgXFsVCOhQC5effSQTKJEv95g/g+qK0RFfNstNd/H9T4dszgdXs63PHyCT4Zd/5Kef518/yK9uDtbg5HwSy233E0WKFQ1bJ9eLQ0Oc5ywpr1PtF/nvI1yu3GlK3anN61Vrk1Mipk7C3bq82wSf1mC5Ym9X48eaNOdJ9U1qNMs5KOMf1fqilhEL5mNi6tTa60cqLTfDayzzZTtdq8M2We0FWX+RSE63QieMBHbo1iKruo/az3AYkfgy+Y2mCIw+LRay0oc69NmqnKB0wOdBZqfZrQ4ec3xmrx4/tsdpKhUXQHxpGVXKXGCZZh2aPKmn3x9vrlJXmjQgrpcT+Y16K8bYkbtB2s/sda7qAWOuknClOvc4NH42XzIRVrZ/vIWm/EqrtS24dIbCqlY+u5lvLQnNYmOx8KpN8c/zQszvDw0hHri7O6iTxGNUJVlYyuUTeWEdM4oHJrSs7+BTm2gYoxssk6OsiOrS8P/xbNkxCIqrmX0plbN48oc0PBVcmud8hjM+wl3vDxZglA2ks+anIbYXT/Gct3P2Z6lfxb58U+6FV3L+l3jmerb8iVQZz3kXz/8Bt7yOxlG03oqnOONjgfQe/zcOPk3NAp77yWCd3vm+ISzJXBON/4w0FW/Ma2zGUBQsOtOO3j41N7wGQ5Sh8w4OfpDscWjem0eZ0mHluwjZ222FLE7LSkJSy+QJfOj1R38Xj7Fl98B61wEYZN5ZuS4kuSyazW9uG7jAvOdRPvgV3vcavvepcO+mM/z6Fv5yP1FIxBspsQVDks8hWQ2ynq/CzVqlRc5U6i/a3K/TiUqcrES3yL3a3aPDC1SYnbePntbtrrxBUiHuAmU26dEpZYqkcpF2I3tsKpTLarXBl0Sy5nmPGV6t235pTXK68wJ2QyOr3Vof1VZg9mqtpGXKzFSsQ9bJyt3ikJZhZtURie8Qbs3xgURwbhzCz7KDqyEmOJzQ0R9pkevtL6iqfjj81SFbdVkygvsDemT82aMWm2m/Jp2DtA+61ARzB7nhGqX92v6CiiFHwgrNduiyqN+YY2IuUGOi1PhmeE6bwkteSEsrIrZsC62JMkOstnL4zX5qknx+IRP6rOpTcU6r4pTKEBNszYS4X0yo06tMhnhe/y7eURQ6uX9wPY+NrU8YQifeKRVUFHPbuvET0x4G8STrfsWTP0aOfQ8z7WymPjcQTFcj6TYe+sLRZQQ9zcy+hAmLA/EVVXHSO6lfyUOfD/tA0xauvJmlb+D+TynY6qtZxJLX8vjXWPOzsN/ehyiZyFmfZOJS9j8+xM65eg5+lO5VVF8dLECJobuw90cUkd1P27U0f5PsOKnlDIESJSM2nM3I6Ckk7tWdpruH+1by0W8O1EXN5oIbdCjEUD4IOTW18u7Phzhe4yBuvUw2JMHc+XCw9FLJYPXVNx4m2oTEiAQPXUOcZ5OcL2lwulLvVesaDbpEeekNisV0i8Q5fEVTYodnna4+N19OpFnOn7TaJi2Hg6J8fd7wKFGkxCQLfc4Mf59XNC5TNEKZRl+kNUuqKnj7BhlrdXhEm045JwzKQEdjxCsd4d6ITVnKY7RFhrSBaiSdoXJQN+Ru3e4fB1/vXj0e0WLxIK7J/kiIO8NCnXqcbJ71duvqM4ZicVeoHdQn/KT2UcUhh8M+3Z7WMYD4YLYSC5SOH/HlckGvc+Xq0KYoimg8NLL4cTriR7tpyfK5BcwtOXoyjMcC0fUvSh8M2SiQ3cc2suLQ+KRgTq8OjQhXbOb0mc8K+aU7AqH0LlCjbIjxJUqC1dWLVEUoZ6ieT0kNFbOCazORrxIpn8qEE1j17SOkR4gDNj7DtLNCPLHQeN/EpSFOWHcSZ37syOe1SwPJVs4ehvgIWZ6tP6bzdspeRvlLQm1folZY3vZ/HrJE7WR20HkXbdeHXnxjSLIoFMkCZC+ycoXJETa28PRWlszNZ3MO483p6AwWYnkf71VlOUvmDb79oQIWdy1t4TUI4uIF1A07qst7X8yU9EIVolgkLacnityuzatVmytltpQbtDgpVuwlsXLzcyFTYoselUqOKvjuFPZ9sUob9WiW9ScdQ/7uvkhImOm1ZnuzmJS0Q9qs12WPrE6FTAZZnaMWqz7QxxTbUEDizojEV4QPxFkaO2Ll3R9xbW7gKcxQbPYQ5vrTOuwZhwclI/KwFv9gyogPRLGUpIS7POUiy1UpU9+H+KYpcrKKQZNwHh7HpJPufPnGYEozlRJOUObBMXZPPoyuLrHHNohNWyaqaxVpZ1cxqwso7k1HXLeXZ9r52Nzgwqws0BrobRNf38P1+/j2DrYeW5F+HBXC4drleWdqFfWtVJeEpoXxeIivHEfk0qT7rX0Oe1jzl2TCYi74SignaNoUElYSJcT7rA+SpSSK6O43z0ZZeloCMfYl0pFQXJOvHVxGRT/R/O1/pbMgLfaIzDZavkXrT0ONX2pBeE9Mzhem50J/vszuUJye2Rw6MvwP0MLsi5gCy6O7uvnxjfzwUyGL86c3hXq88lKWzguJKnc9GrZduZ73voq3v5z/+FO43155CcvmBTWWcUYkN2JWaowhyfGgrJrJXV4+Ne7PLR32b8/YHWU0yZou5WZtGmTsnXHI6ZPiVjyT8uXOFs1yVuqyro83LMLvtFijW52E7dKyCpNsjMRMcLaYlHabPOVqjR6S1ZF3cRa6WD2+0v4jEl8tpsb4RPZIg/KhyhDnKh2Quk8gkie1jaiYXSie0a5TTvkINfptuqy10987zzb1dvSrDVmg1JRBAueZ/HjH057YrXvQgvs4Fg4SEx0cxWKSgcyOQlJMCnFRul3qxs2KTdTlfmlPiClDSv/ux4Mih0dbeMtaLpzAa6ZyTnVQdCmND2IMRLRmgwbnXxv57X5WtwYSPQZU4q04Kf/zavwMbRvqOXM2J83g5jX9Ws8fJ4xwCrE4p7yPSSdx6xvY/xjZnkBI8644sl26jUwXpf2aC8STgcS6m0enNdrdFKzDh77ArnsHfj9kZudQiNpIPx1e/8OQGbaAKCCR/1cQbn+Ij3wj1PFdl28DFY/T1sHnf3xkuzse5NpbeMOLeNVlob5v3VZ+cAMvPG/c20dlZKULSPwoGmS+iiGVisya1eULe1h1KDx+5Qm6i9PWZtIae8Lt/Ku9WcsnZ61Mpg+XwSdSOaUpqtJBsCgmpArsindrSrK/C1FSkZFXZ916dOc7azR6QL2/ejYWSkViZihyQEZ7Afb/iMTXhi7Mi7Enfwe2G9g5DeYpOdyfri9yWD+OTSH36tEqMyLxJcTs1ejPGi0yfcBqabGywz7wvuiSs1/PoOdyrOgYopFSTMw0RcOV+IC4Scq8QkyxLndJ95FnKnKGEheIZHS6WUwx4pLmokexi5DT6Q/5/nUFoC3LLQf468FAeovKWFDGtKLwRGUiDmXY2cXmjhDPa0yPeaF2qXAdPqW3NIaLTpvpz0umhrb0Ww9SVRJcryMmIhxfxFPB0mvdScPqUMYgxqSTQ7ZnL9r3ceApZl7E6u8HoiMkpNQu45lfBWIsFAfWBPKbfSk77zmaNOPJIWTL/peiU9eILraUpBLFw25zGJksv7mdux9jwcyg0NLeyY597OmzMG5qC3HAn/w+xOVa2nlmWzD5r7/96KL2cUBaRtegs+rRGKyAvDTBK2dy6gR6sqFhyIFuXjaTeRXMKOWHm1jdFCqL+iZDlyd54zyqU0wq5ktPh/0/siQ0cInhl1s50BlXVkDyTbs2O/zeRJepcIIS03UdQ2PZ0WKJUhertlGXR7Qe5focDCMSX1rg6w/GaREmpQeHcXUORhU9+aYx44VmGS2yI+g5hHKGi5woIS4pbotJNvRpXbNQ6aDjLRb3OfMKUgAoFDOPquQ7GpX5SMZwa9uUpVIWy9olYepRxBdXqceTIj2S5gtNW7sUOVFcpW4rJEyVsrxw4utFTxRkxrZ3BYvuOKMa2zg8yu2o2t3MnFqScZ7Zzykz/kcQX7aHhic58a0h2eTAmpD5ufQNR5NRuj3E9y79Hhd8lc1/CCUIz3l7SJB5+ldGFQdt2hwyTU+9OrhVd98XPq+eHyzIR7400EX734YYNZMoKiWb4dC+UMNYKNp1SMsMW9JQrGjEzuxHIRISS+pHuJ87u3liEMWd5vHvotGtp6D+ehNUD/isI8uvt3PyBH68mY2tYf15+z6mlnDlTE6uCcQ3YN8MN+6krpj3LGJuOdvaw/vHnmBvV3jMyiVVFHCNGzVr9LAnvMNin3a6X9rrJm02SDskp2vErM5IVoct+ZhgYWiWVSyuVlL7eJQz9OD/Zo82VgdzdcYxaQj17W65cW3H0yVXUPytQ7eEuAbNGrXp7pNAkjhsaQ0cb5G4C0ZQixhPFBVgWWbtkrFDxgZpR7ukIj16/yoxZVKWydgkrkrGNkVOF1Oqyz3HZfzjifvxYZyX/7kE/7a/lfX1PH8xC+qCe/U4lzN0HqR+dUhw6Yu2XRxck3cnRsGCK60LNXW5TLD+Hv0y819KZx8DYsedoQTilPeHmGCUDVbiik8Mp7gyOKIMq/6djnqWvp65l4Xnsesgm246BlfnccTE6XzoF0xbyOo7+dE/0jUK3mjWqlu3imGyuIsUjdikdsyIlVP+ctpvDN0uCkHJBeSa6Rm5VU+PnhG7xsfETFIrLj7ACs5GgaAy+VKzumI+uoR1LaFdZGKIVfeSKt6+gEcPBssxnp+KDqU51HMkf6xEiZoR+gRGIvs0mOhCC/2TUnOVmWOiC+X05EsZMiPGMjNaPOoqzX36bUoWseB0pi8KUkW9yGV44nadB+v9zP68rvPIGJH4avDqOD/LDZ/DFXpqD+567BGNaxvWtGjEthPh92b8hzvlRCqU6OlDvikxNc9+A/pBUUhwPmOrDr8RVy3XL6+2x0pHliJxPVYdJrqcRlm7RdKyjl+91XhhEz6LE4QzeoYQMVi/P5RW1JYHq+84Z3TuuDPobqb7TdJrfs7T1x6xqNp2cdfVIX4XiweZskwHO+8Nz2QvoizbbmPXCkqmxuU6c7qaY3Ltx3YemQ7W/pwNvw01h9DTGl7jFZyOxYKllu4enZV2eP84F7ya5RdycBd3/HR0pAdNWrRqH7YtUVLSHDOH/H5cECul9FI6bhG6zve9ICHtZEAsK9dErr/pnWCQJI+MrJ32DJoA1xfTTFYsNWhpVl9MLKK2mIcPsjx/f6RiLKhkUgknVFLfHSy7zixPNfOSGUf2781V60WVCjWDWJt9kZW1214VTjDJC/JnEebp4HMrbL6NiYv155JlF/C6L7LtCbr7LDyyaal1D3qhtLjANX/WOGJgbcSRJDEjZsS6iBhDOvMyonw1yPggW8DxypWYb8ph/l9kujW2eybfBTghNLr934NI1h7ZQbqMR/00SLP9MkQzxqvj9vHHc4Ukqvv7fzF3IledSnGSRZP46YPHlfxy6SN6xX2R7RoYqs/10N5PMetwacKU4uCLag0smKkr0/aG2RzoYUMbt9ePaZzptoHkPF6YtpC3fJWbvs7aFaPff+ZirngXrY38xyd45qGR9+mPZq0OaDR3GGKLYamFEuJHpzUkhXl3vKaeWCWV7yQxgc776LyV5BwqXhMswq57Q7lHvC5Yh4nJQRw8swVF4bOik0PfwrZfkTti4UUiG20riPgqVQwgvmzETbtCbI/grrxjH8+fyr31wf1ZlGBxJQ8cYEJRiOk9eICZZZxXx6+2sbWN1jS/2xnigb2Yom5Ei69Dl532aLDOKm8ymlZEfZHTo32whsKbHuW319DVepTHJ53udrekakmtsuPTgb1ZCPN8LM5mYRWwJeKhfnNOjCErbkL9yfhipFObb4oL/H/k3XecXHW9P/7nzOzsbN9k03tISCEkQAgEAtIUKXa9dq69X/VarvWqF/VeUexeRa/ixYoKgoJKlSJI7yGkQHrZbJKt2Tp9fn98ZjebzdYkev0+fq99zGNnZ8+cOefMOef1rq/38fYVvaM5JtvQz+OJHGpT/D+EMA9+MMtx8OX+scrPh8NS7OTQyWqTqoKn15NhXPnfpZ1hSEQwpyI8tnSxs4eJpdTEmVXOzm62doc7y3uPYW07q/ezuStYXBs6mVnOtiI7VsRYVhO+yjXtwQT/B8DClZz0Qu782djfG40Fby+b4ap/44HfHV7RTaduO9RbYdmQhBARscQC1aoO0pMsPSuIyuTGGEoeEtFa0mvIN1LzQVKPBcHu5MNkNlD7odDrmNkaPMOqN1B6PKmHKDstTMXo+B8q/yk8On580Oqfs0WP5LDapFNMNMUk+wb0uRVwb7/QeirPtTsOff+Ng8jZXbn50NfuHmCPzTdHxQiiIS3a1Nury05djtZBL6J+A1M/wievp73pwMmUTXP9l1Vv2+h4FfZI2yM9RCnhAYyqgf2JQmhp6I2sDvamAkN+WG8g4O+JTRps0iAprVRcjYqDlAeGI+MOWffar+coEEZMiXmW2mWTniGS14/qkB9NfCo6ncQrKVkRQi/5vWQeIv2nUJLei0gdiZcTfx7iZJ8kdR353uqqEsrehhTJXzpwJCoofze5raRvPPydPgKsxouE8GZvoeMOdDy7j70dnDWfPe3DS7D9rbGwinfM5Zl2Xjmdb23kJdNYWsPDLVwyk0s3MLuck8eF5Et3LhBdeYyyaLggXj2dr+5nXxgAACAASURBVG3knXNDHApOGccPt/1dlGmGQzTGkucRO8xsQD7PrT/kth/T2nB4oVLIyFjjWa900bDLzTfbXDM9ZZ3oFErPpmQxqZuIriI2i8xjQYe79HlBwCb9YHgerSN9b5i2FD8tXFbp+x1qL+b2BBIrZJElNpHYVFL3h5mH2XriC8g8V2TcxgOi36UnEl9I1VvCewYxu7fYYZ9mxwxDMOPUWmSeNcagcXeEiIiMahjwNrs0jrHxfNRYcDoiXPufwePrRT5P43ZxYb77yBnEgFFVdd5SYFwhPO80uAObZ8g+vdBldvSoL1Jc53DokRYVcaGTLTXbXm2u90C/7S0MKWbaJONDNtp+FKakz7XIOWb6rT/oHoL4CqPpsIuMp/p7xBaS+WtoLI7No2RJ+LuX+CITqP4OJSeTuS+IB5e/ncSLaH8X+W0C8b2GfAfJX+sjvkgZZW8J7/s/Ir4UpuJtQrlODj85fa5nTpxBeTz077UnD8yB+7/A2RNDS0cmH/RMl9QEMrtjH7/fzeLq4AE+0hrI8Q8NrC56IntS7OgJ0+lPrwte4ZJqPrU2/P/LxzM+HkKhQp5swnRmHU/d1FAZuW87O9bSOYK2eUmCqceEeoDqYu1HRwuN28M6uvY7KGAQiVJRTc0kZiwMublIlHknkRqQNGmuD9swENEYtZOZNp+Js0iUk+yicWcw2jsOozD4Cc9ISQ/bslBnvOdZ6SnrlJ5Hfg+FaUSnkXhhsOViLyb9WCDB3NYwVSl+ctEjzFL+1nC5xFeQfTYQ4MHIOhBlKYRcXyEbQqBaiFaH63Iw5PeTfqI4AxGFQ7NQe+yzwSZzzRzSu40rcaZTXO+WUd7ijxw1qpzihGFDsAUFT1gzqJ5nRFy5WaotVmGuEuOEmXtNum0uKrvsMWxccPezIVFcEi8mIIv7Xgi6vQ3SFirXI19Mbx2hxxfFa6K8Kso9eW7Mc2Y0jCbqv+q8wpCx1VJRFUeR+EpERqHGFtoZZpnoR27zQieZaWJfO0NGYchK04Ron/UwECGsstJ4k0w12xPuscVasyxwivNERDziDrtsNsM8xzvT/f6svZiHm22hFc4VEfGAW+2xfXQ7HZtLyel0fZrkL4Sjnwjhl3y/GEfZ64On1/7WQIgKlJxG7W/Cld31hdF93hEgJmqCuoOaigsKWrSOqPe3Fv/a7+8I8s/uo66CylLW7WHVMf+37QyxCM1p6nu4chsbOlhUFfJ4BcFbi/a/SRSfl0T44Dy2dAcC7FU0KDiYxIuLl1Vx/tu46N1MnUc8ERZLdrH5Ca77SqiUHMybmjSb13yaU19M7SRixd7jXJbu/exYx1UfY1M/abOZi3nv90Jur7oukJZIWM/Afu07fsL/fODg8OW4KbzyY6y4KHx+oqJon+TDNm97mmsu46nbx2azrPWcPfaZa9aQy8REvcz5fu466XynSPzAsS10kF0bZEXzjWSrSFxAdhOp20mcS0lbIL3c7uAZHjqcPh/Iq1Akvvx+Cu0hz1fz3jDZopAduoKz504Sp4TJGAp03xYGS/ZDt6R7POxC5wwb1n2eU00wbsQq0KOFBY6x2LHDLpOSdq9HBpBxRI1l5nqPyS5UZrqohAOuU15Oj25bNfi97a7SM9T9sFAI1tvF7w/hzd7Pyaa57jIztm40S6mCgh3SIxY/jmos0SkRfppjYSR4fSdHuNHBTex57JMeNDmbEDF+FF3/o0VCdMgK0v7okZKScUmRaO6ypu9/OcFKGGx7K8XUDXFoIqJe4Z2e85RdNnmt9/uuTynI22SNmeZ7uXf4gc/p0qHOFAucaIMnxJR4uXda5xH77JIdi4RbvplCE4nXBHM1+2S4ovP9vdJySl9MbhO5bUQnhZcLe8ntKIY+K/ytc36nOMkVLjuo76dRk3f6mGdtGva9hQFbtwSx1m5rNjXykqVhOkMmNybSKxR/hkO0+DMq3NfMO+cEbw3WFpVqesOTmXzYvgIaU7xgEokoT+8P+bwI5laEZVoybOzkDbPC61u6aM2Il/FPn+AVHw2e2X2/ZdezgYyWPI/jzuBff8x338UTtx28eWWVvPmykGPbvZEHbwjGcrwseGLHnBjIsGeAHnNPe1hX/O5Aui94ayDA+69n1/qDl9385KHkVVLKyRdQXs2au4NH2LWfiTNZcXEozHvXt/jSK6kfnfA+2KXBE9aaM4wnFBGx0klWWeHPd90jfgbZ58g8GTy32FwKW4lUBActfTdS4RLJbQ3EmH2O0pUGLdCU30/bVw5EVtouD6Ocum4g/QyRKjIbAxn2nkfRcvJFzy63m9YvBFm4QiaEQwfB3R7Uos3EYUSdF5tvhRPcZhDZnqOMiIiLnGPCCO1dO9R7sl9mPiJmqpdb4ssqHSsiWrwG+yeZomIqVVuq2hKTXOAZH9FqkCqo+g185y3h5E5U0t1GJk2EaDqpUUxSXlbh6ExgzxU3Mybk+JZEAuENduvcNYQsV6momSPM1BoLapSoGQXxZeTcZ71VFtlgl/39pL4K2Kh70O2tEDVHmQeG0M9M6faEe+zwnLO9XJ0pylRYZLk6k403WVRUm0b1tkgUJcnycp71hOXO9pS/2nRoCcfQyO+i8xNUfIqaq8mtJ3kNqRsDIRKu6uh0YrMZd6P+LQ6i08k9G+awFYYivlGrHg6JhFLv8SYn9wuNZGT93s22GiTbXkQ15mAfFvXbilOxDms2NvKbJ6hO0NYzJpchIzvo8M6DtzsxqrEwCMUqP9gaQpqt6dAFfMPukMcr4Kc7AuEh9qedIqvGy0YjZAsSf9wiPbFWYU07N+0hmQte48rxYacfaSVbcOIFvOQDtDfy3Xez5i/kis5y9QTe/CUueCev/QwbH6OjX2plyjGceH547bvvYsODBzyzaAk1E4J31jCg2LdpF9dfHp6PmxK8xcpx3HctD90w8mFprueK94XP3bedTNEmi0RZfA0fu5rpCznx+WMjvpS0m9zlJV4wrGxWjSrv9c8ebHpc+x8OpBXye8j082xz/ZyK9B0HryM55H7mwlSKvj/7Pc/035lIEP4uXUrJdPZ/98C/8i2kho/1rrPRY9a40NlDknylCm/wMn/x4BFNYx8NpprklS4aMcz5Fw/Z7cAxmegFlvmOMjPkdGu3WqtHdNksKxgHpSaodpzxTldlofFOc4IrPO6Ng4wkKrDsXC54D+OmctVH2Pok57zJjEduc3FrjwLqxJWJSo9wvY9qLNFdeV4fZUqEWQV+mB98LNFWPVLyh7QJRHHcIFI7h4spSgfVBB2ICgkvdorN9jjTcRrtt6PfWPr1ugfV/CwRsVy139g3qJ8QU6JUmZiS4jiRuFd7n5v8XETExf550O0pKLjTddZ6xEu9TYlSt7p6lHudJ30rmUeD51b2aiq/QOnFdPwLhd4yrALZJ+j+rkPi3IU2Cl0GS6wHRAdM4B47VjnFS13Qd6EUFNzhXt925bCjY2oEfc4kXkxf/e08bKgrJvvHlYcQ4oXHcc0TQcJsFOiR1DNCvrZShSkmWmtwS/wgFEjkOpU0dEptDrrO0fKk1E4SJ5Nt6qYuDDwQTSvL7tXVTG4ilZN7FB7uEZ9CoZbUeMqOzUqtbpQtnprxBM9/MxW13HRFCGf2Dyl2NHPrj1j1SuafHB5P/fnA/0sSIRWS7gl9c/3fm8/Stjc8jjYKeTY8MPjrzz0SCPis1zFtgQMh3lHibg/YaqdF5g25TETECz3Pa7zYT1w7uoKxo44CyXvJPEOulfyoFMP70KnLtf7oPKuGVKsJ95jznOpE93n0aGz0kJ/zKhc53sJhia9Dl+vcLFskm1KTLPIZZWbottUGl9rrZhmtBn7pETFlppvtbeb7iFonmedD1viQQv+0yMwlvOzfeOh6ll8UPL9shkWr7Gza5ZHH/2KmUhWio5jnMQriiwhVnY/mwqSGdoZsDtwmqU32EOKLiDhRlQrRo9LIvlC5ihE8viplTjBXrQrtRdnSgaGsjbrtlhp0Tt4qNWqVDJoHjEt4oddpsVezvRrV69JhkeVq1ElLiopa7mzLnK5E3F47rXa/c71CXEJcqc7DGdNUaA6FJ+lbQz6v6pvETyf9hxCGyW8nUkvm7mLYZTDEgtcXCZqefYjWhSKaw0S1Kv/qHX3NxgUFz9jg075k7wCB8IGox6+wHF+nr2btDEQnVDJzHEum0tLN9NHP6oIuPVpHONblypxgsbsMcuceBFWnkmmialXx/cfTcR/xyaS2UXFCWCa1NUSjq04nVk20AjGiifB6tJzypWSb9BFf7eRAZtlUCBfWTDz08zNJOlpDIcrsJQcTX+MOdm1g0Wm885v84b9Zd1+xsOTvyQWR0HUSjYXfHS3FpvjEmHnPDvVucJuPe8+wIekK5T7t/dbZ6EFPHPEuHBbyxWn2h4lb3WONDcO2cExS56PeZY0N9g/o4z1aWGie93nTsF52QcEDHvdwP5WVOmca51RZHdb5tN1+a6hvuyCnx04bXS6qzAKfMMWLbPZtXf2N0DknsG01t/0oPCckrDtbqZlove6+2andR6OPrxTviFIV4d48jxcMOVWpXtpWSdMGqb46ToXZymw4QrHqGE5XO2Kgs1SJcSo9q94ktXZo0jLgBNkj7XEdRc3Og0+wpaosV+XuQaYPduvwiD/r0m6bDTrt9wtfM8uxmu3RrVNGxm5b/U6o4krqkpG21iMmmW69x2wf5YRhEJ1CZGJoSSgkw5EoJIWgc+8XnSJ5LVXfCNWZyV8HDy+SCKHOQnsImUqHxEbiQkqWkV0TwqRlbziQFxwjIiJe7kIXFPOpBQXb7PQRl3p6gMTacHiq+DtW3KvHENnYSEM7D20LfXzHTBhTO0O3brs0WOmkIZeJinihs1zpN7pGcY5GEoHkMvsonUG2JXwdqW2hWLZ0JtlWct3EJ5DvCUQZqyK5mVhFMZ/ZQb6T+DRSxdBj7eQQziwp5U1f4rWDKGTFYkyeHYikakA6qL2Rqy/l7V8LlZkLV7JtDY/+iUf+FPJ12b+RrFlpGbOPZ8HK0MBeW9TpjCfC6+HgjX29OXm/9Huv81JzzBg21zfPbN92qXf7lNXWD7rcPzL2aPRjv7HUoiErWYPXd653e6Nv+99RTXYYC8ap8VkfcJxjh/X2unS70q+196tYr3OGqDJtHrPPbUZj4uSl7HK12d6qzFTVjjuY+FKdIXkc7/WCI1TUMGGmuqfuMk2ZuRImKtEsY88IRXQjEl8S38uH3N7ZkVDdeWchVHUORIesh7RbpeaQgzVVwvONP2Lim6LUGYOsfyBadPqLZ6y00D3WDHro0wr+oMkrTFI2YH21Yt5sqge1H9Km0a1TvS129ivUaLVPq4O7PvcOokq+y2a7DNIxOhJKTqTqO6EsrdAciCq2gPSfQy9fL1I3ULKQio9S9uYQ3oxUBU+u67Ohn0+e1C8pfX4xX7iBSE0gxuxah+MWHGO2f/M+FcoVFOxU78M+5273jWltFXgdVuC7guG1H9vHF8Ocu9uZWMn20dfFZ2Q9bcOwuYrg5a9wllPdOoqigUwDPetINxRJrFC0Q4TahdbfEyk54NUVUkENpqQmkGFfnj8a1pXr55wnykP/XKFwoIhwIHJZGoqnUfsgzvTqO7nsVZz7Rs54dWhJWLiSi9/HQ78PXmDD8HVGY8ak2bz+c6x8WWiL6GihvTmEWzOpkOs7Eqy3yU/91md9cNhp5RERpzrR//qaj/uSez0yYo73aKBMQkZmdENxh0FBwfVu9mov8gJnDnnOlkn4hPfaq8mv3Ch7lMivSqVPep9Xe/Gw3nVBwW3u9Wf39r0WEVNeLELqskl2DN5oUoOUPRKmKDPt4H8+9zDn/DNv+1awoLIZzrmEfE7nxod0yVuvW42SUY2/G1V7agYdBTojxIUhoYOFKvK4XYt3m35I1WUMbzDZtfZpOoJp4y8w3rEjKAj0IoJFptutWY+0vdoO0uuEv2izVpeTBwykDR7MRDdo8gdNffual3Od79vvbz+p4CBkHqLzY5QcR2Rc6APKfrfYq9ffK+2m6zJStxBfRXRCkEbKPXMwQWYeYv9rAvlF68htDiQaW8wY+xcTEj7qPZZZDDbb5sP+wy3uGnOe5fmYJjSw12GicL5tnzU+aC41d7NiNqvrwwyWUeIhT+qRVDHM7MNxanzaB2yw2bYRdE3b7wlkpnAwafWiv0Rjrl/5c3aQ02bgSKJebcxkV2gZ2Pzkoe/pj9RgkxgK7N3KtV/m9qtCJehZr+WE5/Oif2Hecr755rDM0UCiInin57yB3Zv4xb+z7v5AfJlUIPB3fCO0ZhwucnJ+7DcucJZVVgxr/PY2Xf/Ct33Pz/zMdfYMkbM/ElSpMM8c5zrdyZb5D1+3YxBZwbGiSauv+L5lFpls4pD7OsF4X/MZtar9xG91HjKrc2yYapJPe793esOwfZMFBbvs8XU/1HHIZ/bm98dqAPRWXw+iq9W2l59+PJBdzQTqprP5cf7yc+mORp1K1ChRKzaqdNqIxJfAB6NMiwSZsktz7Da0T/CYdk/rPMTrC1ZYjTea4gq7Dsv+mqbUe4YcfnQoMnL263aeZbqk3OJxzYOEO3+qwVLHHrLecUp80TF2SHqynyvfZIAw498DhXbSN4XHiEiTfTg8hkSe3Bp61gx4eez7VqlCTs71btIj6ceudr9HD6vBtg5PYoaQfaxQPNc2N3HJKZw6h7UNYx5Eu9o6m2y3zKJhvb4zrfBdX/QpX7HOxiH3oTA222BMaG+isy00rpdVsf8I5DwL+aCccv9vQ6jzhOfz7u+weBXPew3Xf/XobPP0BaGVIZPm6v8I7Rf9D100FsKdR4p6e3zBd/zMN0wxaUTym2GqL/qoV7nI1X7vVvfYZtdhVUNGRJQrM0mdheZZ5WRnWWmpRSap02Cf/zqK1ev3eth/+6nP+ddhQ56T1PmyT1rpJN9xlaetH7FfdiAqlTvb6T7uPZ7nVPERqCEp5Vuu9KiD+xYL8lIaFBRUOEZMhdwoxi1BwhQJkxVkig3tA9C4nesuCzkAivH6cJLNlrBClQ4545TYd6ShziyuyQcfIIIOw+tuNsv6lb1OUX3IuJ1SER8zy3pd7tA6pttilZhPmm2l6hHDnL1Iy/qNoK47TuWgIYgCrrHPK01ynnGHkPUylX5okX+zyQP2H3HAJII6JVYWDYOb/1YSP39HtGj1UZ8v/lU4olDPg/ig0NpwhpBPvhyaOvnDM4wrC/m+MWKfZn90h6UjVKjFxLzIeY4111WucZO7bFevR8+g3mtURFRMXImEUpUq1KhSZ5wpJtqlwSNWj2lbWxvY8iRT5rLypTx2E+mjQLTpHh6/hccv4sXvD2QViRpUQ7OQD15nJHLgPjMcaiYGku5pLyq6DDhU1RP65fiOEHe532WucJlPqFQx4v0gLu4UJ1jueB/xLk96xoOe8Ixn1dujTYekZLEqsSAqplRchTI1qk0x0RwzLHas4xxrntmmmqRikNqAo4mMrO/7uUXmucQrhpwyHxFRqcIlXuH5znCre9zodqut16hZUuqQMUYxMRXKTTfZaZb7Jxc722lqR3F/zcr5lRv92DWDhJALWj2qIKPWchOdba+bR9zXiBLTvVrCVCl7dAysC4iXhZOspT40rU85JsgZ7VzH3i02SyoRsVv66PTx5bG4mNsrFSTLfpxnzTCs9TuN/tkUpw3i9c2UcIWFPmWzmzQXO/+GRgSzJHzCbG83bdTSZ+NUqlFhninKJcwzxV88Y+0gvWSNMv7TNossMX3AjL6IiFNUu9oSP7Tbr+21XXJMBFgqYpK441Q6zzjnq7NEhSs1HCbxRYuSGLnwXL74u9iV2Pc/DrQu9C4b8bdoYD9aOZTncJnQuB4RxKr3wOIpwePLY1crVz4wJj3LvLxfucEbvXxYSShCM/si81zmkz7k7Z6z1Rbb7dUkKSUi0kdy1SrVqlGr2rji72qVKlQok/BNV46Z+NLJoIxywnmc/oqg0nLXz4oSY0WUlIZeu/FTQyi0/wikyXNC3r9h86FSY+OmBDkyhdC3N5RwdKqb/Y3MWRaa5R++MYQsh0JnWyDWsqrQJN9fzqy8mhe9j7lLx6bYMhSyxZDnJBP8m3cpVzbizToiUhxfNMMcM7zMBdLSOnXp1KVbslggUhATk1CqXLlK5cqVKS3OGv1bEt1gaNPuM75mojoXOWfYnFtExHRTvM1rvMHL7LbXNrvsUK9Rsy49IiKqVZlqojlmmmumySaKKxnVvuXl3eovLvWNIQfnNvurDuvVOMESlyOqyd1yg4ZhI0pNMNMl5vuQiKi9btE9sA5i3nLOfwdXfpDqibznB6GSKtXND95jelOT01VbW2xRSx3pBPbxuDDKN3KhuXhphFdHWZ8bvJcPGqR9zU7/a/EhM+8iIo5V7kqL/U6jX9lrnS5tsn0SYSXCbL/ZyrzAeJeYYqmqPn3OpLwmGTOGGCRLuDhKRM0y0SYN9qs+xOrpj79q80XbfNX8Q3oEIyJmKfN5x3iLqe7R5l5tntOjWaboCxRERZSKFJVf4mZKWKzCMpUWqTBTmUpRvVWPh4XSqUz5p3DHan+M0km0PUjtSjIt4XehQOs9KDD+HFL14e8JF4QSw8Y/kDryPATBcpxqknnmmG2GcWpFRLTrsEuDzbbZba/MGEIvDcUHzBdm8z1XUcqTuygtoSpxWNMZNtjsB37hP31s2Ine9N4oY2aYaoapzrNqTJ/Vfz2Hg6f+zI3f5lUfDyosq14VvMCeDiprmTw3SIzt2cyXX31wS+PSc8J7dqwLMmFNO0MxTN200AO8YGXIww3XlJ7s5rGbOf6soOBSXsWWp0LIsmZiIOMHf39g+YaNPPtwUGh585dDo3rjjlDVefKFHLsijCU6/qzDOhyHoEfSV/1AQcFHvFPVKDy//oiKKJNQJjGsSso/AnZp8K8u9T3/6YXOEhvB+O8Nyc43x3xzjtp25OTc7q8+5PPqDd0ImlRvk284wX+rdryT/Vyrh7R4oNjA3ikiqlSdaktMcJYaJ4pKaPe0zb4lPzAUPWVe0NrLpEL11P59/PRjXPIl5p8i23SLSjGzJDw8ioKaEYmvd2xiK7rQUgivjXSK3aTJD9X7iFlKB3xRERF14t5hmteabIekXVL2y4mgRswUpWZKqBM/qCExp+Ba+zym3TctGHIHOiV1SWnQKitnvV0HTWAfiBx+pkGNmM+aq0bskAupRMQCFY5V7q2m6ZTTKSdZDO5FRSQEHdFyMWWixWN1FK3EWGUoF2y+lfFnBSWW6BOUzQnP82naH6f6BErG03IHye2hjh4S0yk/9oiJL67ECid6s9c41xlmmqZced9FmZeXlLLHPvd5xM9d636PDplbqRDCmwMv6TOwKxrx3HP72NXGillsaTqs6Qx5eT/2Gyss82ovHvEG8n+JTIrffS2Q1ov+JRDH8Wcd0L7MpGjZzcZHDyi69KK5Pnhgi1cFrzFa3M18PhDnmr/w28sCKQ6JQhhHNHUez3stz38r50eCTZVNc91XDya+7nZ+8ZmQx1t0Om/9Svi8fJa92/jl50IV6Ud/ETzDo4FO3b7i+/Zp8u8+YJrJf3eP7O+FzbZ7r0+73Ke90kXD9tb9LZCW8Tu3+KSv2GGQ2UYHoWC3a5Wqs9C/KzXJFBeb7CKFPi0wxQKWXkcgp81jnvFhnYO1oPQK0lbXcfJF3PbDQH5te6ms1Srr1xo1y+o8GsUtzXimwJdjQaqsBL/KG9F+Tyn4qh2mSnijyYOGKCMiapRYqsrSfrqOQyGv4E6tPmeLmRK65IZVcCkR9WpnWGKWbik/crueYZLaKQXftUunnM+Za9oQHmXwBkLxy99/inuBTLFBrFAIpYU1J1M2m9QuMs1BH7CQJ9tG5ZJAiCW1gfSyh9E0PwATjPdh7/JOlwxZYBATU6nCfHPNM8dLvNDP/dblvjdoM/tS/BuH2JEzcdXZx7JqbpAr60yHHN8j2w9rdE+r/T7pK6pVutA5Q+ZN/hGQ7uGOn/LoTSE/NuWYA9MOmnYGImnefSjxPX03l14YxKYnzgwKMJEIXW3hPTs3BON5JHQ0ByHrO37KrONC+DTVHQh325pDl9/yFJe/jmNPDgZ6JBKW3fp08P5KSvniS8N2HK3BGj2S/sfV1tvkUh+2yooRCzP+X0ApKqO09ruHb1fv/T5rk23e503GF6Mrf0sUFDRr8wM/921XaRmkr3kw5KVs9X0d1jrG+9U5Q1ydiFjx7hnWnZfSY6cGv7fN/+gebAAtwUq78N184KqQC1h/X1Ber5vOpkeLTlIJknrkRszyjSigEBF0FGtRE2FfIXh+MUbVoTFJ3H86xptNVVZk98NBRt4tWnzUJpv1mCPhTsvNH1Cefqtmr/SMpLxxKr3eWZLSuqQ8YbPNg1ULDUAM5xrvP8y1Ss1RHanUi7yCr9nhU2Odjh6tpHQC6UZKJwcCrFhArpP03iA5lu0IDWPZ/VQtJddFzzYqjyOfIrlz8Lr6UWCqyb7hUq/20r68QK8IdI+kZFG6vFxCWTH30vudZ2Xd6DYf8ln1A76HJULrwsDJ62dDosTdK+eE6QxP7+bi4/jZI2QPv4hmlmk+76Ne56UqRpEjOlxc7gc+5St/k3WPBrWiZohbZ5gEXRGTlThJuXoZa4/CSK6/N6aa5G1e6+1e6xiz/64efUHBJttd6J9tHaR/d0mcLVmSoyT8ZaW8spL/aj20mLBU3IXO9kn/YqUTlYwyPzcWFBSkZTzkCZf7H3e6b8yVor2IqVBloWrLVDpGiVoFOWn7dHrWfk9L2mXY4WyRKAtPDxbYmrvZu4XSCs56PU/eanJLoxUq7ZfzuM4Ra0di9JXjDYoqfCjKxAiNQn/Vv8d4cTQUHYxU/N4t7x5tGmUsVGHcGL+kgoJ9Mq5Q77O22Fm8gHN4iQnmDiC+TXpcY59ssbowIycqYoFpHvGc7lHcAArYKulWLRplTFdqa1WPOAAAIABJREFUnJJiacjhn2AFBd3yntHlfzX4qT1axtp0WsgEQitkw+98N6mdgfRyXUEKpJA+8Du1i/S+4vP6sFz+8GJN1apc7jMu8U/ixVBLStoDHvVd/+vbfuRHfukXfutGt3vWJuONM9mEvukHC803Tq273X/QhdQsTF4PdXUHHo3YnctLFwo8fyHLpgePb92RiU2263SXB2xXb7bpJhgvegSG2WAoKLjfY+5w31FbZ3/MFjdJiZZhbhjHK/Mmde7UMWJWuUrU+aqdodIdoyxB/0dCp24PeNxt7tWi1QTj1aoWGyRtcTRQUJCRsV29G93ucj+wxoaDqppjWBjn4+OC91aCtnxRvzjO0lLShdAnDbNKOLmUaTGWlHJ7D5NjrEgws4T2PN2FvOdsdbO7NWg0zWR1ao/K+VtQkJTypLUu931f9B2rrTuiSu2CjJQ92j2t2T0a3a7Rn7V4QKfnZA0YDDnEWjTvDInuruI4plwmCFX3dJgibrEKlWI2S/bJlw2FURHfK6JhCvY50VDVOTfC7/I8L8LDo7Bg0goe0+HPWu2XNU6JSrFiscqhX1WwNgp2SPqdRp+22dX2au93gWcVLFVlljLtcn2Pdbr8QZO8iAJaddipyZO26JYa0xfYKedB7f6o2friJIcyMYmiw27QrT+wDwX0yNsj7SmdrtXocjt83Q43adZ8lGWGDgeVYoPOHRyIiIi3ep2P+RcJCQUF+7X7L9/2Kf/lLvfbaqc99tljny22u98jbnaXWtVOcJyYWLFicr7Nth0kZdZLdAMxSxCwbmlPhqb1Z3YH4utKH7HuZFrGauv8yZ022yahVJVKZcUQ91gNtN51tmqz2Xb3etjv3WbzaGcuDoEJYmYpdYoKy5TbKWOKEu8wwTLlomiRHdTKnS7uTFXyWCBhj4y0gslKvFC1BRIaZSUVdMmLiVgo0Ud8pSLOVOlMlcaL2S0rguXKnaNKmYi9ssaJWSjhVBUWS9hb3J4lEs5Tbb5SjbJ6FEwXN1XcWSrNVWqHjISIs1U5VYUeea1ySnCqCmerNFWJ3TLqlJiv1CqVpovbLXMQ7YfQXKu/etgNbvd4cThqaDdJ9EUpDocger2gFm3W2+Rmd/u2q3zZFa52g422HXJ/qYhwYQUXV9CZD6McN2a4oII3VAcCfFUV69Jh2S/UIcKZ5ZRGuKmbs8tZFOfkBKckuC8ZTv1O3R72lD+6w1obRYW2hnKJMZFgb7Rmu3q3useXXeErvu9eDw86WPYfEXOV9Y2Ti4vaM8J8hhGD4XHsKnBnnmOiYTZfAbsLJCKjF5vNC9MQ/sNWV6i3VKWTVFmgwiRxCVFZYTjsNkmrdXpKpx1DsHdUuSuVulW1mLhuLVpt16xJWsEU481Q5zzL+k7GmJj7rfPgGDQyC9gp5SoNfmWvGRIWq3Kq+U622DTVIlq126pHh6xSMbPt0uEp623WZbukJjGl5iiYpk1KYdCwQcR4cyRUF2WVh1IPiagw3jhzVBgvJ6tDgzY7ZQcJUfWe/r0jmCJ6R01FLFZjjf0y/S7YfsOM+l6dY6YPeHvf6J6MjG/6kW/50ZBTFwrYZbfP+Io5ZnphccBmhXLv8Wa3uEtTUQGnHJMEebLp/bb5DNQvnGTjtNrizkQ4fio/fvCgUsaomBJliMjJyI3Csw/bWFBvjx/6lavdYL45TnK85Y433xxTTFSjSplEXz4wJy8trUdKhw5NWtXba4sdNtlmm11226tN+0HVrCXKRIe45LLFaWKD4STl3muiG+3XIScmSOrNEtcmp7bPiBwcCyVUizpOmblK/UyLj5lso5QSfMQkX7J3UOI8TYXXGucWHeqUiOEUFd5ovDt1usR4lWLyCj5rip9ocbwyc5S6QpOZSqXknazCAgnf0OgC1c5R5Vbt0sX9ucR4M8Stl/Ixk11mr/Fi3mmCG+1XKyYu4jQV3qbOL7V6edGfu3EQAfK8gt32+q2b/N6tJppgvtmWWGCJBeaZZZIJalQrkxAXFyvOjMvJSctKSoZ2h5I2u+21PrvDc7bYbId6DVLxdql8flAthUgkKOx15bmhi3PLuaqDfblwT31JBT/p4LEU76/hBeXhf7uzfHd/+PtVleEauj9JQwkLS8P7SiP0FA4+f3/mOtf6k9lmONFxTrbUYvNNM1mtamXK+vYvI1sUbm+zU4N1NnrSWuts1GDvYYc0/y/R6+VtEqr+R+KkURW3NOBzsUCAwevhgzGeKgzfzD4Y8kK7Q4O0P2sVFW7AvV1oueEjvSJi5jrDGT5gjtOVqxMRldGt0bMe9RNNrrFPm4ysJ2xxf9GzmKDGOZaOifj6IymvXsJs79DhTVabaZ24rE67POovLpfHa3zTbmtc7fXSxd6VaU70JtcpUerXLrF1kPBXTNxFLrPQCz3pV/7gwwaaFQk1lnujFd5sogXiKhTk9Wix1X3u8x27+qmmHKvKuabYo8cDmlxsugjutU9CzCkmWKfdKpMdr1ZG3hNalYtZqNpzOtyvyctcaHFRsLag4ClrXemXw44a6kWjZj/0C89zWl/T70mOt9JyN7sTTMYFQmjztcXfBI/vqpNn0ZEKRBeNDJhuHjDHmS7weVFxa1zvPt8ezVd6EDp1W2291db7ueuLTcyhjyuhtEh8vSH0jJS0pJSUtIzssC0qUXEX+k+znX7I/wry7vF16/1xiPdGbJP2K61919taSU9Lqpd2wwiTJ56VdJ395unxcZM9IKFW1M+1KBHxXTNMF7d1kO+yTU65qDoxd+iQUnCeanfocKN2XfLOV+VOnTZIuVabFSq8vji49DkpiyV0yplbrO+Oi3hCt2uKhRIVIk5X6cv22ixtXtG7Xa1HBFOVuF2HjqJHulrS7+zXKuci1f5g/7A3uqxcXyTifo+pKYt46Ykl8qmEB9aWKWRKlSgp9sj1fr9ZKWm1NSmvOD3toW0Z9z134FNiUV68LFTM3vDYoZ85sZoJlWwYJBcUi1AeCWFLQuhzSoyuYsizgI7i6LeaKJ8dz7ZMILvhspY9kp612bM2+60/iRfP3+ADlhaJLxitSSndeiSl+sYJHS1ExJSaJK15CAN/8HeVqBJRIicpP0ZPs13OM2PQgR6VcstP81wrqGjkhKnscyOsPwqVWXlGrecYEbXMP3mJr6sx3X71dnhIVkqtmWY42XQnmep4t7tUi073Wdenz9mk3d0GKUcbJeIqXODzTvdeERGNntNqm7hKk53kpa60xT2qzZCwQ/+mj5i4CnVKJESH7CGLKFOj0kQJVYd402VqXOhLVnqHvJxGz2rXIK7cZIuc6PVmOdXv/ItN7hDB+aa6T6OtOlUocYxKV9ioQ6YYGokVBwWXSwuKyMuNd4JxntPuHJNskPIyF/R5PAUFN7hlxFFD/fGIp+y02yLzEcbHnGOVW9yloGA7rhIGz36dPiGks1H44zN0p4MZHYmwZvchkmWVJpjjTCVK7faU0cciIv1+5/ueF4T8ZUp6xJFGo0Gk6M3PsFxUiaiSorkXjucTfjns+zuG1B0aGXm9FXRhL3uNzALFDtSh25OekfRf9rpItc+b6rP29E3OIBiqvWH/3m3MK4gIYdbPmuKP9muWM7tvqwva++1Nb+Cx95XewddbpV1qjxeq9gVTfb5YENVrYOSE3llCa+e5S0KbZyrDw5tZOZ94jMe3suKYYC/1pFm9oyCeyIgkMpqinRbNZd5kVu9gY7+aq/I4L1/B9Mk0r2XaOM5cSFMHf90QSO20cDqbPYFT5rGnjce3ccoxNBYFhnKFcHynxUgVAqltynB6IoQ/T0pwR3cgwPPKQ55veYKyCOOjIbd3xX5OKCU+yghtQQi7p2W0FQdqVy4NE0LSI9f3HREqzLXcT3TaaKvv2W8EsVmB9ha51ARna3aPdT6j8Dccsjuqut88B6W6m9E8yDU3zxJzLBArrrZdm0fdZbq5VnqBnIz73apdq+Odap3H5OQstdKznpIcgbGnWuZil6kxzdOudacvabFVQU6ZcZZ5tQtc6nTv1WKbB3zvIFHqtKw9Wkezy4NikYuc6u3gPv/tr76tW5OoElMscbGvWOEtDhFYPQqIiDrF2630Tt2a3OLfbXCLlA5RMZMs8iJfscD5LvJffm69dvU6Zc1WKS1vv4xWaW3SCpgioVapycrkFLQXrbNyMXv02KJTg6SpplnST+orFBE8NqyHMxAtWu1Q30d8ERFLLZZQ2lcJmsOjem/IAQ8j0pVmag1vXEF1GZsa+fXjg3zK4EcOoY+xkD9YPTpayfjz6XyS8gX0bCS9O7yW3E73egcIdGDAuP9vgywXCWIBmSbBi8i61Wc96PvKjFNunBO81iIXjbgHvaQ1EE1yTlJhm7QNUoP2LxUwX8KFaixRZr2kTVJ65L1crbiIVjl7ZByr1EnKzVLqNBWe1mOmUnPEPSflNJXKRPxVl5eq0SHnIjXu1tFHoL2fmUeFqHJRzXKWFnOR/f/fi255T+jxKrWe1mO+UtdrM1vcImU2SzlbpcriGk5T6QWqXKDa48Xce2UZC6ZSXkpbN/WtJEoCGUUjgZgqEnQm2dVCR08gxmWzeM1pbGvkmEl88xZyxY1LZXl2N5ks2/YxvS54ei9ZzlPbD3z10QhvPJOeVFjnpr20dnHMZB7bGjy5P3XxlmpWp7m6I4Q931bNR8axPs29yXBMnkjxr7XszPKXHhpy3NPDe2qoz3Jz9yi7ePqfrkWUzQq1cQcR32jtwzGgxonGW2m8lRr9eVTEFwpg9hlnhVJ1trpCt21Hd8P64ag2vJzrZbIyTrDKw+5wmvNt95xLfNgj7jTORG/wQVf7jpd7m22eldTt5d7qh744LPFFxKz0DnXmabDaLT6ttV/RQKe9HvYjNaY6z6ed7j3WufGgZY4EJcqc7E1KVdnmPne7XHffNPeUnR51u0u9xe9VGmRy6BGi1kyneaeomL/6jidd3Xc7zGG3J/3Z581wshmWW+RCj7rKH9U7yXjVSuzS7Q57+s7zceIe06xaiUe1HFTkklOwSLUuWceZq64YuoL92jVrUT7MpIOBKBHTMaABplfvMNkvH1cleCT7hYnsZXicMIOvIxnGEVWXhbtNfoQrNjGHquWBwKqXhwu8+cYDVa1Vy4MZ3P5AaAeJVVM2l9qz6fkp415AyTg6n6JyGcktRMuIT6J7LRVLijOGBAWdrnWBYBPT6VzD5NfReD3x8cQnamp/UFPmwIyx8eaOivjWSto7SP7vZu2iWKrcliGs423SvmCPOUrtkXGTdvvlfVOj81XLK/imRt0KasTslfVH+9UV84YZBcdIiOEHmtTLaJBRULBEmdu1u1eX8WJ9Xtw2ab/WaquUn2mxRJn7dWkreoT3DKDoAn6mxQWqzZPwfc22SJuqxCxxZSJ+psVzUhYq86yk2Uo9otst/c6ptu5AbD0ZTp5LSYzuVPD6mjtD0CCbZ9WCMNgjlQ1E1pPm2Qb27g9N973IF8Ip11Fc5/lLgxeXL4R1V5dTVUZZPOgp1LcGoutMhvVWJoIHms5yXRd/KJJWFntyfLWNkkio6uw9k3/UHnJ46cKBY/PD9uDpZQpDF4H1Icr4c6ldFQq/914TbLhxZ1OxiK5ilqdyCdWnBNss24Y8e64O7zlS1FgqolTSLvvHMAy4zeNyeiRMVWHe/zvEl5Oz1qPGm+RpD5lrkTkWONZS9bYqV+kYi1WoEulXdRTpK7kYGpUmmu88sN6ftA6iuZmXscb1TvOuYir79KNGfDWmme5EsMHN/UjvABqsttc680L32VHFLKea4Fid9tngpkF9gL3WabbZLCvNc7bH/cx+Gff0mxP4bL8bxbM6Dvp7IHYVDZGLze5rXyA0sP+Pr45J4T4i4jgLD3qtQsUh0mEXCP2hu/FhoWc0jr/uaCGZ4az5tHSNTHoQryPTSM+mQFa5/Qe3ciS30VkdvLKyYsd1soHOp4MQwKTXkNpB6ZQgFNC9jrqL6VoTvMV8MvRSxmroWk3FcWEdZXMDWSa3Bg+yekV4Xjav6AGODQ2yGgYhvlY5vxghgtEi535d7h+gk7hTxk8GjNZ6Qo8nBuRWOqRdOUBPNpBXl3v6rXOvbB85t8hpKa7n9kHOr02DnDed8n43IKTcIOuqAdsYijkO3fauJE9tCzm3TC54acfNYFczW5sC+cVj4bRJZVg8nQc3BsLrTgVvcVfLoaSyp+1ADdUDzzF/Mnc8Exrwp40Lp+S0cVzzUPD2YtFAdpOqwzJTa9nRHNabGrDynEO9t8GWyw/y2lCIVTH1n2n4SVAwzBY7Bbo3MOmVlB8T7Leqk4J9VjabjsfDKdp829EIg0aVF4PaPXYOPmVhCCTVy2qXMFmZGUe6IcPiqBJfCMkc+Ol9rV2rtR6VkXa3G3Tp6GtkiCuVGIXnUGO6GtPkZdR70lB2T5ud9tut0mTTnWS1a47KvlWbqkKdnLQ91g66TFq3Flv+JsQ3zYliSuXlzHO26ZYfskxMXLxYPFJjhpiE/BEO/sUh6iwVyp1p5RGvN5g+B6frq9GGl+AapDEPf23pZv4kdraydyTphOK5kWkWxPZypHcFYurZGAiLEPZM1RMpDeSWT9KzNZBUrj14gmJhmWxrMIeTO4JyjuYgE5dpCiZ1uiGIB8Sn0L0x3HEyLZROD3ea+CTaHzziY/b/dzypZ3DiTPHkABt3/TCqfOv6qW41dRz63l7s2R8esGZnePTiTwMieDv72Qc3jDYSf5SR76b9Iaa8jra/BtuO0L6b6W+/5IM9WMjRtZ6KhUHg6UgREVVSHE6Q1iI/BiGEnC45PUIpWO2Rb8wwGDXxxQUZnaShtf1zsvLysjIK8rKydtlily0WW67ZXh3a7LRZp/1e6DWyMmrVjZgvKjdOiTI5Gd3DDIENRcj7BRXyKSKiQ2RIxoYytWJK5WQkh5DtKcjpOYIc4lAI+zJVRMQ4s7zCFQYn/gNEElN6CKkc7mdX/s3Grxy61mfwHoH8HsA5xeeOn8a8CWzYS1vPsPGevnMptUNEVFyVkmSHfPIh6f5FItnW8IDmPxVfzNPxCAq03a1/7i6mVLxrh6gSOWnpltsV+ntiqV0OSq603hae9gzMBx59xCTEi+0S4dpLFTvpxl6xFy2WYvUaWhndcqP27gdJLqFEQrwoJJ2VltF9GNdlxG4l4spUqCzeaZKjbl0ZaRtjSpWqEBGTlZLRM6bjFyvWUYbzIyOje8gWlb8VCll2XxWCDjPeG/7ed/0Qy4aqp4PT1Ee+BXoZIjIqVef+GDnyd7QwKuI7NcLro6GReA9+kjOo0NbdbtChTYt9mu3xez/WaLefuNyJVilTYY+d0pJ+7huWOV2bJt/yCW0jVgj270Ab+gZSOOibPJqSRb2fP1SpQUD+CEqDB2/nD//p7f/qtNcGt8iPUCbcZNOIy4x+uw7epjbtHrNa9ggv6noNB+X34DHUC8VU+wXySxLiVxMqqSkLSZP/r737DrPrLM+F/9tleu8z6sXq3bbk3o0bzRgwjg2EQEIgBAgBkkDgJIEEn0DICSeFQzMYMMUmBhvjXuUmyZJtWb1YXZqu6X323t8f79ZoJM2MChZfckX3deka7VlrvWvN2utd9/u0+xmFR0Id1oComAnOtcjNxjtbtiKD+jTZZoP7bPWIviPccEdHnQIiKHWWOd5imksUmSAmU59OjbbY7De2e1LvkJsuNeI4p4P0ouKqLTDbDSZaqkC1uGxJg3q1abHLPqvt9qIGm0es8RyOXGVmuMpM1yk3Q5Z8Cf1a7fO6p2zy63SIYeS/pUC1y3xGpnyrfNc+q5WYYoF3muYyhWpExPRpV2e9De6z07NjXldEVKFxJlpmqotVmiNPuai4QX061dvvFZs9aL9XjvvML/AuM13roB2e8y8G9ChQbZ4bzXRN+vvN0KdDk+22ecx6vxzjGiNKTDLbDaa7QrFJ4rL169Jsu80ess1jun9HfTfjRVTeHJJYonEGWonmUHpNUCyM5QTVwuRAEHNK9gYh8USXN6RbWUoyXcaQkqVaXIH+E1yYZCqToQAJAyeoCXqqOC7xFeE9UX6apDbFogi3Rrk9eeR9iomaochajfbaDnal6+XaNFvugaF9Z5moQYvH/eKEL7RPm0F9MuXKHsMMDu1ACwR5sIMnlXk4Fvp1SRoUFZc5qqB2RKa8UbYd/zqi4jLkjnDk4b+l3QEP+osxrd5Dx7wRL9uUlC7dUlJDBLjLHr/vEw7+ltZtSuqYOGEc1UIxe9iHl9G5v5WX94aUu/7EcSy+hPP9sSt9XoGaI7ZNtMx877DOf3rI57WPoTQfk2mx33OFv1RuxjHZulNcZLFbbPWYR31RnfUn/Lf/tsiQ6yIfd5GPK1Tj6JXyoXrLc/y+dgfc44O2eXyU0SImWupaXzbVJWkhgMOYYKm53maZD3ncl210/4iWTI5iS9wmX5Vmr8uU762+rsbCI+5dRMRUl1rsVit805Nu1z+CRFpE1FIfdLFPKnOW+AhdyEPs+C3O82HP+YbnfGNMIp3kPOf5Q3XWe8kdKszyFl831cWi6Z57hzDFxcpMt8lvRhwzKm6eG13lC6rMPUacYLILLPRuOyz3iC/aZ7XTafUTCKxtRci1an+J7nQyS/cmdqclYxOxoPPc/yzRleGYvr1HuUKPQsblDK4hdVyB5pR2G5GUZ5oiizSma3XHRkSpi8QVG9R9WhNbOAHiyxaykDalQqJBPMVFUYrkGK9anmxb7VehyF4NKpUYkDDfVP0GrLPTFNVqlNpotwGDJqpUr0VU1FyTFcn1mp06xohHtavVqU6F2arMs9H9I+5XoEqhGikJDUZoBX2K6FSvV5sCNcrSKflHIy5LsYkjbkulLdVIWmNkJGQrUqB6xKPrbZCUUKBGgWpdIyTXnC7UaTyC+PLliYkeY629EbgMtwoPZgtq8A9orC6koYMNxw+WT3e5MmdJStjofg02SUqoNNs0l8lVZolbpSTd5xNHWX4BUTFLfdD1viJbsX5d9lrlgFf16VSoxiTnqzTHPG9TaJx7fFD9KPHfNxYRZ3uvq31RhlydGuz2gibb9OuWrVCpaSrNVmySfl2a0ovRkTDBud7tDlXmShpUZ509VmhXJ1uBcRabYKkai9zkP8RlW+tno3o+IiLmeptzvF+FWepttMdK7WplKzTRMhOcI0exi/2Zg3ZZ5buOnqspSRFR5WZKGtRgk3obtdipX7d8lSY5X7X5CtW40uc0e926E1hQZysyzhJv8kUTnKvFbg026dIsQ45SU5U5yy7Pj/h8REQtdLO3+T/yVBjQY48V9ntZj1b5qkyyTLX5aUtyvLt90D4vHffaThjZRMeR3EV0OqkGYnPo7aNrOdGJxC8l2UbP68TmBuJKtZP1JiItDG4kUhVC1NHx6A1jRnJIbECc2CziF5HYRnQqkSwG1zHa+qLFCv2aZKo03ae026jvOKrOBeaZ4sMiYrps12nrmPv/tjgh5ZZ9qdCWqFUoXn8wSbkyN7nE/V6QL9vVzvYTT3qvq71ooyss9qx1smR4p0vsUGurfbr0mm2iPep16TVOmbPNUCjPQ1aNeh2dGuz0rAqzzXa9Vb5zzMs/ImKma+Wr1KHObit/y9tzGO0OaLRVoXFmusYadw6pshxCiUmqzBvx+D6d6ZdSsXIzbPXoMftMcO6oxBleRPsVGW++mzTY/DuLH+ywS78B2ekVd6kS1Srt9cY0sx2OyfihYPHtQKmQ3PJqBMumhNS89l52No+6pplgqSZb3ecTdlg+tFqPy3KWq93oX5WYYqF32+LhEROgJlrmKn8tW7EOdR7yOev9Ul+6GDi44Ma7zGec749NdK5rfMk9PjjM7Xl6kK3Q2d4nQ64Ote72QTs8bXDYQiQmU54K4yyWJU/rCB0DCO7N6/y9KnMl9HvRNy33de0ODBFbtkLz3eQ6X1Gg2rW+pMEmB8aoz5riQoP6veDfjxovIk+Zy/2Vi31ChhxLfcA6/6lnBC/GRr9WbNIQqXRplEi7M8N3UOMaX3aO98tS6GzvtckDJ+DWLXGdv1diksd9ycvu0u6ApEERUVkKlJupS6ORHrQq813j7+Sp0O2gx/yNV/xEj1aHFrj5Kl3oT13iU6rMc72v+Ilb02O+AYiQeT39D5N5JX2/CuQVP4e+XjKuJVUfCHCglugE4kvo/xWRQiLFZL41kFz/A8GqSzaG4xPrAwFGS0jsCvvH5ofxU23phOlHRr6sDhvVe8hE71fhWot9x+v+SatXDOp0OAYYl6FYqYvN9DkF5kkZtN/P9L9R92gUHDcINojvJMO/J5KhE/tD6edgh1qv2K5Tj0wZCuXKk+11+62x1QXmypThASuUKzLHJH36tekSEzVeuTkma9Eh/ziZnUmDVrlDu/0mWuYyn5WnwiEXT0yWma51kU+IiHrNPZpOUZpsJPTpsM49Egac5cqhF09ARL4qF/uUIhNHdK92qNdku4ios71PhdlDFlRU3DiLXe4vZYxyH5ps87IfS0m50Mec54/kqRBNK89HxWUpVGmOhd4t9w3sKr3VDo3DYhSFCiy1+A0bfzj2CV6GfbgFV0tH3xo66erjrArGpRvMjYCIiIR+T/uqrR474gU4qM8WD1ru65IGZMhN12Ye6Z6OyXK+jyg0XtKA5b7uZT8eIj2CNdJmr8f8rS0eQcQs15np2jf2hoyAbEWKjBMR0WBzOk52pPWd0K/dfpv9xlp3j5qkMdsNQ1nIWz3icX+nzb4jrLle7db4kWf8k4QBpaa5wEfExmyGGrHNYyOMl9KlyXP+RaPNIiLKzVJqyoijdKj1qP9lkwe0OzBEemGkpDb7PeNr2uwXEVFpjlxlx72HGfJUmedJt3vKV7XYJaE/nZc+qEeLvVY6OEI2QzRdU1xmmpSEFb5ppW+nE9tSQ9fWoc5TbrdOyC6Z6lLzveO413bC6CG5m6x3MLiJ+EIiuUKz1PSbPVLM4Eri5znsDc8k2RzH+0UfAAAgAElEQVQsuEhhet88IiVhWiW3Mrg+PVYeic2kDhIpIlmftjDHeL0k9drun7R7TURMlRssda8LPORs3zffNyzwf53tRy7wiHP8SLGloMGj9rjT6XYJn1BySx/WHXUdnXrsSdeHdehWq9lVzrZHg1KFJqpQ56CUpBnGS0pq0ma6ccYrl5LynPV69BqUtHdYrdlo2Ge1x3zJDW53sU+a5lJ7rTagW5mzTHWxXGW2esRy/3zEJHkjsNY9ZrrWXG91g3802w0abJIpzwRLFZtot+dNHaGcoU+7NX5gonNNtNT7/acdluvVqthEk10kadAuz5vi4mOOTxr0nG8oTVsqb/F1S31Ik20G9MiSr9B4paZI6PdtVx83Dnii2GO/V6wzQY2IiJioG13nLvdqG0YGbwSeFh7KfpQIndmfgLaeoNdZkU9d+5h1fK322OYJI02elJRNHnCxTyo3w3hLFJusYViniBKTTXO5iIhmO7zmnlGJo0eL1e4ww9Uy5FjoXTa67xgieiMRVCRDnVyxScpMO6X4YlyW+d4hJsugXi/5ftpiORYpCevcY5k/VGWOGa5RbLLmUVyog/qs9oNRx+tQp9Zrqi2QKW8UF/+hc4+d/dlqj4N2KDFJlnxZJ9DUOiLigFe87EcnnQRWoMZM14iIarPPK+4a9V3Tr8tqd5jnbbIVWeBdXnHXMd6iMRETutImBdXrYY/14Jrg5kysIzqJ2DQSe0PiSiSbVCS4OJP14WdiO6kmdJF5BQPPhv0yrxKItE5YeXYGUk1tJuMaUi0MriLjYlQz8NjYl9xhg7U+Yp6vKXG+TCVKXaDUBcfse6ghbb0HbfQX+k+AC35bnHId3wHNatMv1gEJP/f0UEA9JWWXurSMUcr9XkivxJOiIr7ubilBPPa7HsSwFPQxkJKwxg/0OOhSn1ZtgYmWISJpQId6z/s3y319jM4Gp45uTe73Z3q1mePNZrvBHG+R0K/Zdg/5K+VmjEh88Jp75Cl3gT9R5iyV5ghiPT0OeNVj/lapaarNT6dSH4kuje7zSXXWO9t7VZpjvCWCtmRSQp8ODbZ7fNQXzqmgR6+73e9NLpOTbtp6oaVucoM73T1Us/lGoFxa2QK/Gb5hbnUQT1yzlytmsuvgqI1oD9qpa4zJ06FOoy3KzZCjVJlpRxBflbkKVElJqbVW+3HiEwe8qkOtMtPVWCRPpbZRXItvBLo122m5KnOUmeZm3/e8f7PVozrVnXBCV54K1eaLiOhQl9Y4HR3t6X2qzFGoRrX5oxJfpwa11o46VlJCV9qLEBUd1dMxNoY3Oe5N/yZ6QmU8KSk7LB+6hpNBuRmKTAD1Nh1XJCPEJfeosUCVuQqN13QyMawbivirah5u53/XBQmXNJK19P4rkiRagotSMrgpUz0kXic2nv77GVxhKHk5sdkR8rSDaxxZ2pAKY0sxeCgnJ0nf7iOPGwstVlrtPca5WY0b5ZkpQ6FI2lOQGlIS3WC/n6vzq9OezXkIv1UB+/AJdojwDmG4pG4QrU2lW5hE3TGsE92JClQfHjeos+ywXI2FSk0Tk6lTvTrrHfT6G27pDUeLXe71UdXphzhTrnYH7POyNntd7/ZRjx3Q41nfsMH9aixUoFpSwkE7HfCKLo1ylKZjGUHj8Wh0a/a0r3rZj1WZq9hkGen06Tb7NNmuzf5TqG0aGw97yvNWucolDrUW+l/+XKNmD3vyhBXeg2urVK4cu0dYnFwr9H48JoWleyCUM0wvD9IbY1h8HeoMjlF3Nqhfqz1SUmIyFR2lElFqqlhaUabZ68e1CLq16FCvzHR5yuWfZuI7ZP3XWGCSC4x3jnf6lgabbfYbmzyg1roRMyWHI0/FkEu8U/2IMbYjzzug2fah+1bmrFH37dak+zhZv4ctuePXb+UoVmaGCjOVmCJPuUy54rJlyDXuJF3vKck0aZ+8S63YpCGibrHruPHEXu3a7VdjgRzFCo07ceIri/FnlRTEuK/1CNIbwnASSk/DZC39vySSz+ByRkwHSB173IjbE0f97iRuWa9aO3zDHnfINk6WKnEFwoK/XZ86vQ5IvAFCGyeDN1S5ZSxEcZ0CZeLuPEoX8lTQpdF2T3BCqbJvLAZ022ulvSMmz4zd5DIlodn2UVfKPQ7ad9xShaQ2+06LVTsamrX4iv9rjhnGpYvpJ5vgO/7Jd9zl5+6z0x49eocWQMEtGpMjW7lSs0x3mQtd7RIPe8oX/eMx59mBKShmiG56kdjaEPQ5S3J5evuYxNev6zjusZTeoSSViCyFR2wdHh89EXdxQv9Q5l9od1pw3GN+WzTa4qfe7yIfs9B7FBmnxkI1FjjfH9tthTXutNWjoybbZMkXSycs9ek8oQVjIMdQkJ83RixtQI/kG6Cun63IQjc7x/tUmStb0bDSiEPL7aToSQvDp/Sf4ss2R8mQVdmj5bgWdqirDM9bTIbso563MfH2YopifHIv606iVU+S5LGqjieESLpvX0ZaMCRpcKiUZCQXfugzGUv3lBxtAdxvwH69dqfHOFw1HxmWpTCQXkQMP/dwHOq7mZQ47oJjLPzOiO+/JaIxJkymoZbe/x6diE8Y4wu4YDyra8mOB4tqz9jxuuVW+KKv+qovKlMiIqJKhc/7hA+6xSbb7LBHa/pFmydXuVLj1ZigRqXyoUa2zxhZvqsVb8cVDqsE/b9JJdbGY8yoCAKIl02npZuVu0NN31E42drNYxcqwz+f6FjDi95/N+oTLXZ60F9Z44cWeKc53qrSLDlK0wXVl9vmMY/6G7VDjZ6G4/B1Hu2xGQ1H7jPWAu9kfTnHIkept/iaxW4Vl6VPh71eSnt2dupxMF1fm3CFv1ST1tI9cZzaFUaOum8ndqbhL/qTENZ4tpPnOtne98bmexzqT3WUHki+Cpf7uFobLHWbelu84h6X+VMZcj3s7+1Lu8RzFFnqvea6TqZc9TZ7zrftH+bizlJgqVvNdb0chfp12+tlz/g3HRqc5RJT0vKHs1xtlR9L6HOBD9rvNY/4ip60EtckS13ogyqcpV+39R6w2k/1jqE3PBpOK/EViqqRYVBK3Sg2XgxV4orFdUk4kBY8OxolYirFxUS0S2hMCzKNiUiEqvE01TF4Cqn/+QV87mv865fZOHb8478VIrhlLhMLuWlWsKS+fGxj3KORkPBjv9BvwJd81lSThqy68WqMV3PMi+BkSWATPnrU7w4OJIKLMxZlT9p9Nr+Gg90j1vVlyhuKN4+MyDARgpS+o1yCw+OjY4klHEIQNQiZoQkDJ5e48FsiaVCt19RZ7wX/YYoLLXKLs1wlV6m53q5AtZ+47ZgMxX5dQ1JkmfLEZBx3FR3uR/hOT2/ZRsS5PmCJ94rJ0GCzR/y11z2tV9sRFn0oifjQabyWI3Ho/BGxtPU2thxdRGwo4SZp4JjnbUTkRLi8gDcVUhmnYZBH2nmmg97fggGPM26mPEu8W5mptnrKFT5pgsXWud9iN7nIh93t42LirvMFM11ppTt1OWixG93mO37gvRrSrtylbnWVT3vOt7TYo9gEZaYOWeilJrncxz3vu5rt8Ha32+IJGz3iCp+w1VM2etgkS93mO/Z51Qu+p1CNS31Mqcke9HdjhjZGwmkjviVyfFm1abJ0S3pJt5Kj3BHFYj6h3NsUyRQZalnyj+odSJu4UbxVoT9ToVR8aJHyhA5fUJvuxjUK8gv5yF/yf7/EwRHqQiLplpypEcaIRgNx5hcSjx+7LXmKCR0VuZTmUN+Zll0fZCBJSTZtvQymwjwqyaajP2yDspxwbFsf9SfQnSAzRk76uqvz6BqgtvOwHPxDr4ef4wvo6mfzsCB/RjT8PjPGgQ46D7vABgz6qV/aYrtP+WPXulyp4iGCG9vNG1RgNtnmhVEKeQcxHkscVm1pqG1P36dEyOwk3X9m5MVMvkoxmaO+xGMyFBnvkG5kx1HJKy12SugXk6kkPUnHkqLLViRfJQJp/i7FBQ4hJalTvfV+abOHTXWR63zFBOeaYKlF3uOpo+LPXRr1aJGrVL4K2YpGLNY+hIiYUlMRCLfFztP292QrtMBNaTLu8bgvWe9XRiKYqPiIqi6nCy32GNQrU15aoixzzCzerGFZq306dB6vY0FelC/U8Ptl7OoL5DQ7m9tK+Y9G/rHu1MgvP8oXa/hAGbv7OTDAzGx+r5TvNnF7Hd3BnbjBg9a6z1zXaXPAct8Ul2W2N4nLNM58S7zTL3zKa+4DOzzvYx6yxLs84iuIqDZHq/1WuHNoXkTFj3Bh9uuxwp3ylVno7db6lS0et8iNyk0TleFiH9ap0S98Kh1+iGhzwNvd7lX32uPkVMFPC/GViLldjRxRf2KfVgnvVORmxVam/eoxfFy525T4e/Ve1mOKTF9U5UtqfMI+3VKqxP2tGs/p9D0HJaRMkylDZGyLb+5irn0Hl15DTxddHTx+P5vXkZ3Dm97O2RfS3cnD97J+TSDA8ire+ftUT2DrejKG1SpNmcFbbwn7bFnHAz+n/SSykC4Yz+cvCo3BmnspyuJbLwfS+eer+V/L2dFKQSZfv5p/WcXaBi6fxJ+cE8guN4Ofb+SnG8cmv7Or+eS5tPdTmBVI8461/HRDGON985leEqy9nDh//Qyv1odzf+Z8FlYE0u3o5x+eD9eVRlLSamv9kU8720JvcbULnGuS8QrlD7UwGpTQq1eLNrvttcY6y63wqvWaRomdXSQot6wUFj0fxffxwsGj4jFbGkZdZJeaKk/5qDHQPOUq0i2SerVpPsoSqrdRl0ZFJqixUK5ynepHvdWVZis0TkpKk61j7vu7wKAe2zwuKsN7/VymfBOcmxZZP7wy7tSg3kalpilQo9KcMePGecrS8mMR3ZrVD8uEfaORrUhhulaxU6O90sLhI15XeVq27XeDJtu0O6DcDJXmKDBuzEVAmelKTEbIOG4bQyYPIa73h+V8vY5vN4U27SUxvjSOT1TyYhePnkIZ0Y3FfLicbzXx9XraEiF++OdVfKyCDT38LHgtOjQMWaet9qdlz7uGxO8nWCJLviqznO8PQEZ68VFj3tBicaOHzXO997nDGj+3zdPajhK+6NWuT4cMOXp1aFeXbnHQJy5bjkITnW29B4bF3FN2WyUlaYIl/zWIb6FsS+T4iH2eTpv1+w24flhQd5wMv6fET7S4S4uk0HAzT9Q/G2eBHCt1yxCRK6JZwk592iStO5GgZn8fTQ309bF1A20ttLYEK+6m93PJNfz8e1RU8+kv85XPsGMrH/4sRSX8+qcsvZTx4YFVXsXn/pFXVvLor7j+nWHb//kbBg9bREH8uF2rvUcq2mfH+cRSntzFd19lYSV3vpUfrSMeDW7HzLRFHEt/zo4HS+9T5wXSemIn547jS5fywj52juFqyo1z/ng++jDP7eXGWbx3PvdvC/G8b70SrMdkitsv5+0zA/HdMJ3FVfzpI7T38WdL+bNl/Pnjx5QPdOvxnJWet0q+POVKlSiWKxsR/fq169SqTZt2vXqPG6aYgl/gUJlQbfp3Lxy94xgDlZhsqou96mcjbp/mMqWmSUkNSWANx0E77fSsRW5RYaZZrrPGD0c8aYYcS9wqU76kQZs88Dt1dY6FTnUG9clSMGJcaVCvDX5lpmvSLq7b7PKcASPFsyNmuEaF2VJS9lipyeun8epPLH0wImqW64fKC34XaLffNo8rc5YSk8zzNs/71xETqmIyLHKLXKWSEjZ7aOxSo5wIt5Swv58fHqQ17WloHOSug9xSyvWFPNZ+cjG/nAjvKQnW4/9rDOMdGvebjbyjmPeWcV9Sqic1ZJEF0ffD1tkhf06uYhlyzHHNEdZukx3qbBr6vNlj7vR+53m/a33elf7cc75lpTuHjktKOCTpeEhA4Mh7mClTjp6jXOsDegzql3MKLYxOC/FNlWUQW4fdkBaDdumXm56AE2QoE7NGzxGPy3o9iJgt20rdag34roM+osxF8vxKm4e026V/7FKS7ZtCcsr17+TZR2lKr8LzCrjmRr7/DZ55OLgt5yziyrfQ+kMWLePv/5x1q9m5jcvTHbIXLSOeyQ//PViJdfu4/dtUj2ffrsOn9UQ62/QolGQzsYCv7g5W1NoGdp6AtTi5iFmlXDQhWGF5mcF1OaFwbOKDXW2sOBBclZuaApFmxQLxZcW5YUIg1vEF9CXCU33JpECqe9Mryod38I9XUpxF08gJPikpHTp16LRzhAbBJ4JyXCq4Oa/EOMHiuwz/fpJjxWW7zGc12uqAV49Imx9nkct8Jt3iqt9aPx/KuDuEQb1W+JbprpCvypU+p9VeOy0/YlJmyrfMhyzwLoR6vuCOO70oN1O1BfZZpUPdiNmYh7IhcxRLSaq3fsT9NnnA2d5nmssscJNar1rpu0eUQkTFTXGxK31ehhw9Wqz0bQOnkeB7tGl3QImp8lWa7EItdh1BLjEZZrrGZT4zVH7yu0BQkfqe2a5XbLJLfEqzHbZ65IjFboYci9ziHL+PiEabveqnxl61xZmRTWGUr004slNtcSy0Z5+YGVxmJ5O2UBznrGx29FF31HNQP8DrfczIorTf8QxS6NSkU5N7fFLjUQuglMRQaCApYZeV9lijzBTn+4AbfFGjbbZ6auiIsTCgR7c2BSoNj6dmKZAhW+cpyJudFuIL8brUEUkqKfRJDYl8ZaSTWI92Vw4ItX3Z6bXFAP5Fo+U63azYHyvzEWW+psFdWk6+k0Z2Djl5h4kwmaR+PzUTyM0nFqMtbU53ddCZjnmUVQarsS/98m9LJ1kUFp/YeWORYG0eitklk4f/fwiHllORSHBBEqzAgSS7WmlLT6pX69l2AqosvYMMpu/Q8J5bZ1cHK++h19nUzNzyw9syY4dbThPOHYsEK/Q0IKIYSRnalQlZnS8xlHqykuA4zEgrVySSgbT7Rp71SQn1Nqg0x21+boNfOeBVCQOqzLXIzSrMQsp2T4wqaLzL857xNW/yd8rN9Ht+ZKNf22OFPp0KVJvhame5SqY87Q54zN+OWL8XlaHaPDlKZKaVRbIUmOS89D3gLFc5lGjTr1O/Lj1a1NtwDGGVm+Fm39OuTq1X1XpNqz36dYvLVGyy6a4w1SUiYpq97jW/MNILplODR3zRe/xAqWmu9WXTXWGrx3SokynPJOeZ660KjTeo1wv+feTF3RuIPh02+rWJzhOX7VpfVmS8vVZJGFCoxgxXm+dGKUn7rTHeOaf1mobjgFc94R+82VcVm+TdvmuT39jleb1a5akw3RVmula2Ql2aPO5LY4qFg6xIsM4ikUB+R3fLerSdl7tPPsMzMxLG7kkeW7OXSNGdJDtK5onN891WSRo0xzUa/YdBfYLGaWhlFZ7ZiBxF6SSqAY22e8ldznWrIuNO+NJ7tdvmaTNdrtQkB+0WFTfbmyQN2uPlE78PaZwW4ms0KFNE6bBkloz050PfV5NBPZImHqX3VykuU8QBwxMqUlbqtlq3CTJ8TpXPqbJcl11jZvOkwgM0XNextzsQWnlV+ByNUjmO+gMhFphMBFcn5OSSm9ZxbKqjsIisnGDxHSK8jhPMbGvvC/9mlrK+kfJcatKv90MEWJRuBzOhgMr0EuFABwd7eK2R59Mv1czYiGn8J4ylNcF6+7fViHBjiHdJYm0951QH67BvMFiZtZ3h2k8QEWUyXSClQ78XOWIVfI6oaimdBrwq1wdAg1/6zlgKGIvGhyScXQe5bg4PbAjJLkehV5vf+KxZrrfMH7rUp9Orz9RQ25iUhJ2e9Rt/MWoiStKgF31TSsplPqNAjfN82DIfSmf0xdOZo0n1NnjEF9OancciV6l3+o4qc0TFRcTSCiNDKx2L3Gyhd6WLChKSBjXa4g5vOSb5JqFfRFSFmSrMtNDNUkPuouhQxlxKUoONHvJ5ddaNemt3e95/+mM3+N/GWWyOt5rjLZIS6eVpGK9Loxf8h2f9n9MqySZ99av9wARLzfN2JSa7zj8Y1CslKSZLTFyrvR72BXCz753maxp+dQlr/AgRV/m8YpOc6wPO8X4piaHvmJRm2z3m76xzr+MyVm+ahLb38f5dQaLsaCRTJ987rzcZxiqIkRE5shA+HqEwRlci7HcCqLPZU77hch83xfma7ZSrWKkpHvIlO7wgJsOb/Z185UNZntNcqFOj3SfRpSIl6TnfNtHZ3usO2y1XoMosV1jum+ptPqlbwWkivvV6tEt6hyLr9OqTdI5c82UPxed26bdCl3cr9pgOBwwoEPN7iu034JV0nKFAVKmYeoP6pdQatFaPaxXKOl6qfHtrILYrbmDn1uC6bGoIySzv/uDhZJYZc7jnDpobWbeG2z7K/T9h8fkUp4uZ164KSS+3fSTsc8M7eWUFdSfgFyC4N3+6kY+dE+J7FbnkpUm/tZeXavn8haGublJRsNZSKfZ18KP1fOEi1jUEEu9PcPsLYczRkEwd2bdu+Of1jdw2j89eEK5hfEH4HfxyCxdO4J+uCtd1djVfXzlqBuVIyPImSbViJolrNzhMxT/DeQZtFLdAQq2kRgkNEmMlhBRmc+0cCrJDa6L6jmD5DUOXZrs8r80++6y2y/P2WpWWdpstM70SbbPPJg9Y7c5jEzkyM8nNpa2NVJCSe96/2u1FZ3ufKS6Ur1JU3IAerfba5jGv+mmId+Vk0nPsG+kQiQ2OGDsbHa32BddqdnaIVaezj/da5V5/YpZrVGQvlpc1Xrx9QDQVkZTQr0ur3bZ70mt+ock2Y71wU2nr90dZt1g88B6zk9cNqZMkDOhKJ5e8Ev+Z3ZEXJQZGiLFHI/oLk/Z0r5bbX6TWa8buXp5y0A47PStpcMQFSKcGv/Qxe600z43pDMqsdE/4ZnussMr37LNGmWm2elToGD/6fT4Uv01KnpKLbDgS+rzkDvusdo73mepSBaqHykLa7LfbC1a7U70NI8YAj0HLIBt6WZbHhMyTK1ofc9xEGOuCPCZlsnnYdzgxM7g5X+nWc7DV877toD2SEl51r450Fupea4asuaAe9C0HrDPPDYqM06vNGj8bivElDVjrXnOzb1Qemy/V1Wmbp631Kw22kZnhQHWrFU2/MNDdp8tBz/mWdrUSBq3Ou1/D4Cb6aLbDXf7QEu8y3gI92v3Cn9vqycPhh9wcyss4cPzytbELUE4RMfypCp9WYZ1eLRLKxGSL6JZyk536pCyS7RsmyBKxTZ9xMlSJ+4Ja92uXwrly/IeJGgxolFAkar4cv9bmr9WOnS4RjXLxm0L8LjEYklm2ricrm6vfxrJL6O7mkXtZ+xKpZEh2efcfhPq/l18gFueFJ0NMb/J03nYrlTUhYebXP6X1JISgM2PB2ppazOstgcz+dXWIoxVncdHE8PPV+uDq3NMeElAyM1lUw6yCYOVsaOL1fsoXsu9FUiN8yeUFzD2L518NroyCzBArXNsQXqBn14TPbYVsqiXVyva0+7YylwsmhOzPV+rYepBoNgXjaTmOqwbZbpWwS8xUCZsNDMu4yvUxA16TYYE+j4ibI6Vfv+VGbfAFU0oD6Td20jtwTKLNIUWHlOQRL74MufJVDhFfV+yg3ullUtu3HVuSMm4c77uN795Bc/NR48flKJWrZIj4ujWn44MpsrK4+d3cfU8gqSMQkSHnOIXLQQ1l+P9TUgYiPdxyMw8/QsuREmBRcVlTFsi+5Y9k/Pv3RTp6pCT06UwXd3c7qel90wxerJVdG5WrfIj4erTo0SK5qCQ8R8+NsNjLjoncOk98c4/ICweGehyMhZjMdIeHSFr1Y7SXVUS2InnKh4ivV6tuLZIGxGSoNFVzuvXRoJ5RazhjMsVlSkkZ1DfGOY9EtRka7RpV3SYiJkexGnNFxLTYq9tBlSZrsEPPyYi531DI96eE7M2v1IbSgwjK4szJZkUX+05BlvGaQn44hYfa+EpdSHQpj/O56pBJ+qFd3D+KBysaDWGggfR54/HgHUshGhOJxqQGBoikwrbB9MI9FmPJIpGiYp58RioeTScDRrjuaiZN4NcPU1sXjhs+/tWXc6CWdRuJxxgYJBIRicalohEG+kMIJhoN20pL+KPf50c/C+Q3Bk4L8UGWiEvkuVCeHimPpolsnAyP6hha+0yU4WoFpsnUYNCTOm3SO7Q9T9Rl8i2QrUhMh4SX9Xhel84TFUeOpl84R7/ootHw5RxTx5eOsY1YqzfWtpNAdpx73sG/rwnEdzQy8qlaRG8rWQWUzWbLr+hLJ8RE48x5D9t/TV4N/R2BGIqm0tNEfydFkzmwksq0osVgL/k1tL5OVjGxLDLzyaui/lUGOimeSutOiqeHMbpqqZjPQA/lc6l/JWwvnUFn7YhEGFUh0yWSuvR7xnBCy/VRKQMS9uj3lKhKmS4x4EWJsVydk9Lu5z1j6z8eg1iUG24Iq8FNW8jK5MN/xH3389hj9A2zmiMRPvoR/vNe2tu56koKC3nqaUpKWHouO3bS2Mi554Tn59HHOGs606czcwbf/g7LloWxnn6auXOZOJFXXqGtnfPPC8/bS6uZNJG1r7FkMTt3cdml1NWxfDkXXsiUKbz4IjfeGIi4oSFcy6WXUFTIU89QX8/H/5Tv/yBYqqeCmSWcV8PcMr6/nunFlGbz7D72DKvru3AcV04KSVWP7w7JWnPL2NrCylounRjcZk/s4YJxYdw19TR2h+N6BsN+l08MMdtNB8Pi7spJ9CZ4ZCdXTQ4ZyRubw/4XjKO+m6f2DIUEYjIs8WYZsuzxmgIVLnarVe61wZNHZCFOdY4q03Vrs8Vz5rtKriLrPaHMJDVm2m+jhAETLUDEqx40wVyVptqeTpl/i89a5zFrPWyKJSpNtdULSoxTYryEfhs944p0If0Kd+vTbYk3e8m9suSZ4zIxcRs9LVeRSRZpsMNWLxxpDWZEQg3fX1aRH6N5MLypi2LBXXnzDtaegiWYEeFDZfxFdQhrNA0GLdDMKP9SH2oE+0agg8xM3n1j+Pn405QWs+wcOjpZt4ErL7Ep2xoAAAfcSURBVAvvw6eeDfPtgmU0NPHiSt7+ZqoqWL+Jrm6mTGLnbrZs49N/yq69/OQeFi9g8kS272DPvkB6kyby64eoKKOmmtc20NrKJReGufTok1y4jCmTefpZNmzmwx/gwcfYN7Yn7rQVsPdJeVynx49SKXjtqFX9XgO+P4YeYpekB7V78LdpfzMaSY1KXqkxauTG2nYSSKWo6xrdhZhVSOEEGtZRODGQTP+wl1AqSXIwkFflwrA9v5q+dhr2E80IFlpGHiXTAiF27KPjABMvZbCbvg4KxtG2h+oldNZRtZicChJ9gSTzqyiYwMEtDHSRV0lmAfEs6kYOKic16nXvKNua9HtBMp06lrRf7yhlB0egLI/K/NCeaCAZaiFPBNFoIJa7fsqBA+Tns3UbTz9zJOkdjQXzufwymg8Gstq7L3xn113Dc8+HZ6e1lfOWMWsW9/6SqVMoLg5E94M7yctl4cJAsje9gxUrgivmZ3cH4po9m23bAzl291BRzpYtVFayeDE/vouurnDeFSu5+sqwIr7ichqbuOB8fnXfid2HsXD1ZJbvC7HlmSWBfGo7uXgCPzmcmi4SYW9HEFC4ajLj8vjZFm6exeZhczgikGc0Qmd/ILo97VTmBSKbUcLdW7hmCs09wSNx8XheqWdJJXdtCnPjD+bz2K5AqFMK2RYWfTFxky32jDu0adCjw36bbfbcEaQH48zSodk2L6oxU74ya9wnImaWi6xwt/PdrE29Xp1i4sabrcpZ6X4nXTodVO916z1hQK8MWXIVmeUSGbLs8rLJFstVqM52rWo12o2UmHi6ZVi5HAX2Wm+mixSrscLdlrlJrS3ah7tdB1Lc0RSkyi7KY2pWIKrdfSGxZeMpalQOpPhOU7AkLyugOk79YJBFe6179CzRCHKyA3EN9HPjm8P/J46noyMQ4PqNzJvN/trg8bhgKT09wfLauZuaKqZN4Y4f8wfv5aWXWbWGtevDM/32G8LC9JzFTBjHmleCN66qkmuvZNNWzl4UyK+jk18+EGqsMzPT82xeIL4TxBmtzv+/0Jfgc0+FF8NISPTTuov+9mDBlc8NZNibtnhyysK/7LQLsGQ6zVuClVc8NRyfXRLIL5YViLGrnvY9wVrMyA2k2LKDjv2h9COnlO5GepvpbiKnPE1y2Yf9ArHMYPERiPBk/2wPSp2KuGx7L2+dz9wadjVz//pj4nyjoqOT+rowEWOxsCotKwsTa/jip7CA4qJATv0Dwcpa9VKwxt5zMxvTBdvJJLW1YcKWlwdiKisNYxMss4MHA+lGI1RUBNdPMhlcOi0tYcIWFzFnNvl57NnN+mKuvYaf/Cy4dirS2qRdXcES7OkN52xu5qWXAoEXFYVxysqClTqSCtHx0J+gLDus/PsSgYxermf7COU2+ZlBeKFnIKgMVabbCcUiYYx4NJTMrKplWU2w5pp6QuJWfgYHe8P4jenMxEsmhIVMfyLcq47+QHrdA2FRWJaTTuY68rvu0aZV/VBvwrhM+Uq1HOXmHDSgxQE92g3okyVXgQo9admxAhVp92ifVrVyFIqI2eZFM5xvjku96OdSEgpVyJLnLOdptEuG7LTTtU6VTkT06VKgXGZakzZbvgJlIqJa1erSoswkCf0KVYiISowUC00KcbjNp0hyoyEhWIsnYzGmBMvuvHOZN4dde8L8WPFScDHG4+HZjMW4+Hy2vU5/f/BALFpAIkF7RyDEJQvDM9zXFzLOBxP09oUxX9/F/gPBujtrOtVVgTh372Xr9vCzqJCDLWH88TXMmRmsy2R6DlZVMGl8mC+Do8eYT5ur8wx+W0SCpFoq/eXFcwKZHfoczQiElEjHkyLRsD2aKcyaSCCpwd5AdKlEsBJTybRUW3r85KCQ/RpF2l+eTPvnI1EmX0F2Me37qEvH6pJpH31q9AfrdNyOIZd16iSs7miUBQvYuGEoRuDccwLxrFx1ZBB8xlnB0qqrY83LzJ8fJtPqNYwfF8iloyOQVzIRriEeDwkxkybS2RnIqKyMrVvDuWbPZvIk1q8Pkz2/gJ07w0ti2VLy8mhqoqmZhQuC63Lta8yexeQprHstkNv215k2jb17w7aysnBdFeXhOvfsCe7TxCl8J9W5IeGqdzBkD08vpiqXlxsCCR1CVS5zyoI7c2VdILrZZbzeSlM3F40Pf/OLB4IbtCaPDc2B6JZWB1Ld0By2bW8Jll97f4g1dw2wrpGpRWGfwSQVOSypCiS5tnHoO4+Km2ievdZLSoiKmW6ZlKQdVh8hL1dlui6tOjWLipvmHPlKbbdKnmLVZqi1NZ0U0ysuU1JSiWqFKu2xzkH7TLRAgXI7rTbZEtE0kRF10D4larSpF5NhmnPt8Zq4LNOco0OTOtvFZejWJk+JQf0mmKfRLgds8V/6NRyPB2suM5ONW8JcnDMreCkO1IbwQVd38KZkZlJeGubJrr3MnhHm4IFaRJg6OZBcfQPjqgMhdnYFy276FPbVhlDC3NmBXPfsDeNOnhCO6+kN19PYdPi6COPFYsyYHtyg6zcdjheOgDPEdwZjIxoPFuNgTyDNMziDMziD/+Y4Q3xncAZncAZn8D8Kp0eO4wzO4AzO4AzO4L8ozhDfGZzBGZzBGfyPwhniO4MzOIMzOIP/UThDfGdwBmdwBmfwPwpniO8MzuAMzuAM/kfhDPGdwRmcwRmcwf8o/H/g35M0bflWAAAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "print(\"Nuvem de palavras para agressivo não:\\n\")\n", + "nuvem_palavras('não')" + ], "metadata": { - "id": "jDhWSmiyvEn7" + "id": "Y2swNWf13ngt", + "outputId": "a11358ca-59ff-47b6-fb39-a88c96e1b4b6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + } }, - "outputs": [], - "source": [ - "classifier.predict_proba(x_test)" + "execution_count": 280, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Nuvem de palavras para agressivo não:\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", + "texto_preprocessado" + ], "metadata": { - "id": "S6sCRdMWvEn7" + "id": "kbd6vgVyuI4Y", + "outputId": "16a2b234-9f10-4146-fcc6-d19a0e309b3a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + } }, - "outputs": [], - "source": [ - "predicted_proba = classifier.predict_proba(x_test)[0]" + "execution_count": 281, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + ".. ... ... \n", + "829 não CesarNi85939384 \n", + "830 não PauloRo49195361 \n", + "831 não Marina92011959 \n", + "832 não Marcos_28_11_66 \n", + "833 não FATIMAC75843178 \n", + "\n", + " tweet_text Tamanho \n", + "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", + "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", + "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", + "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 \n", + ".. ... ... \n", + "829 [rt, claudeluca, alguém, notícia, vão, cassar,... 1127 \n", + "830 [dindorio, seguindo, patriota, sdv, fechadocom... 732 \n", + "831 [betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas... 687 \n", + "832 [rt, drbots2, justiça, condena, influenciador,... 1154 \n", + "833 [camelojubeni, konigmachado, marcos281166, kon... 958 \n", + "\n", + "[834 rows x 4 columns]" + ], + "text/html": [ + "\n", + "
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Comportamento agressivo?tweet_authortweet_textTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
...............
829nãoCesarNi85939384[rt, claudeluca, alguém, notícia, vão, cassar,...1127
830nãoPauloRo49195361[dindorio, seguindo, patriota, sdv, fechadocom...732
831nãoMarina92011959[betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas...687
832nãoMarcos_28_11_66[rt, drbots2, justiça, condena, influenciador,...1154
833nãoFATIMAC75843178[camelojubeni, konigmachado, marcos281166, kon...958
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 281 + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Twh075x1vEn7" - }, - "outputs": [], "source": [ - "y_test" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "cidades_519 = df_roubo_519['CIDADE'].tolist()\n", + "cidades_str_519 = ', '.join(map(str, cidades_519))\n", + "print(cidades_str_519)\n" + ], "metadata": { - "id": "HvtvdS0rvEn7" + "id": "QC-dtUu3-HrT", + "outputId": "a7992a8e-f335-414f-c645-8cfa64771cac", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], - "source": [ - "np.median(classifier.predict_proba(x_test)[:,1])" + "execution_count": 289, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 289 + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2jorQC_rvEn8" - }, - "outputs": [], "source": [ - "threshold = 0.6\n", - "predicted = (classifier.predict_proba(x_test)[:,1] >= threshold).astype(bool)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "#Definção das variáveis preditoras\n", + "x_lista = texto_preprocessado['tweet_text'].tolist()\n", + "x = ', '.join(map(str, x_lista))\n", + "print(x)" + ], "metadata": { - "id": "LqsOJwBLvEn8" + "id": "NnrjeZh05D5F", + "outputId": "cc88698e-4a9b-4dc3-b9f3-e70fb700c741", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], - "source": [ - "np.mean(predicted == y_test)" + "execution_count": 290, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "IOPub data rate exceeded.\n", + "The notebook server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--NotebookApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "NotebookApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0M21byuKvEn8" - }, - "outputs": [], "source": [ - "x_test_geral = x_test\n", - "dtf = [x_test, x_train]\n", - "x_test_geral = pd.concat(dtf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "y = texto_preprocessado['Comportamento agressivo?'].apply(lambda x: 1 if (x == 'sim') else 0)\n", + "y.reset_index(drop=True, inplace=True)\n", + "y.head()" + ], "metadata": { - "id": "0KZc6CgBvEn8" + "id": "eXJsyZoo4sVy", + "outputId": "a66329dd-538d-4dff-ff6d-d1f23981ebc6", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], - "source": [ - "print(len(x_test_geral))\n", - "y_test_temp = y_test\n", - "y_test_temp.reset_index(drop=True, inplace=True)\n", - "y_test_temp[y_test_temp == 1].index\n", - "res_geral = classifier.predict_proba(x_test_geral)[y_test_temp.index,1]\n", - "res_sim = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 1].index,1]\n", - "res_nao = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 0].index,1]\n", - "\n", - "np.median(res_sim)\n", - "np.median(res_nao)\n", - "bplots = plt.boxplot([res_geral, res_nao, res_sim], vert = 1, patch_artist = False)" + "execution_count": 283, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 0\n", + "Name: Comportamento agressivo?, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 283 + } ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GCvfdnSFvEn8" - }, - "outputs": [], "source": [ - "pd.DataFrame({\"Não\": res_nao}).describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "vetorizar = CountVectorizer().fit(x)\n", + "x = vetorizar.transform(x)" + ], "metadata": { - "id": "_ayLrQFJvEn8" + "id": "-ZScVlIt5M-u" }, - "outputs": [], - "source": [ - "pd.DataFrame({\"Sim\": res_sim}).describe()" - ] + "execution_count": 287, + "outputs": [] }, { "cell_type": "markdown", - "metadata": { - "id": "g1biI2dKvEn8" - }, - "source": [ - "**Comparação com as predições do Botometer**\n", - "\n", - "Visando a avaliar a qualidade da classificação dos modelos gerados, os mesmos usuários passaram pela avaliação da ferramenta Botometer, já bem conhecida e amplamente utilizada (apesar de sua aplicação com enfoque nas publicações em Inglês)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NADjnw5qvEn8" - }, - "outputs": [], - "source": [ - "#Lê os dados da aplicação do botometer\n", - "#Busca os dados dos usuários avaliados\n", - "datafile_botometer = \"data/handles_inct.csv\"\n", - "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", - "#Preenche os valores NaN con 0 apenas para avaliação geral\n", - "df_botometer = df_botometer.fillna(0)\n", - "print(len(df_botometer))\n", - "df_botometer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dREze2TlvEn9" - }, - "outputs": [], "source": [ - "#Avalia os resultados do botometer\n", - "a = len(df_botometer['analise_botometer'])\n", - "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", - "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", - "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", - "botometer_geral = df_botometer['analise_botometer']\n", - "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", - "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "

Classificação do tipo de bot

" + ], "metadata": { - "id": "DzmZgqDkvEn9" - }, - "outputs": [], - "source": [ - "plt.figure(figsize =(20, 10)) #(11, 6)\n", - "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", - "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", - "c = 0\n", - "for i, bplot in enumerate(bplots['boxes']):\n", - " bplot.set(color=colors[c], linewidth=3)\n", - " c += 1\n", - " \n", - "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", - "c3 = 0\n", - "for cap in bplots['caps']:\n", - " cap.set(color=colorss[c3], linewidth=3)\n", - " c3 +=1\n", - "\n", - "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", - "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", - "plt.ylabel(\"Avaliação do Botometer\")\n", - "\n", - "plt.show()" - ] + "id": "T5zR-UdTtiFE" + } }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Mc5WWVrevEn9" - }, - "outputs": [], "source": [ - "import scipy\n", - "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "#Lista as funções atribuídas ao bots\n", + "funcao_bot = df_handles['Se você fosse atribuir uma função ao bot, qual seria?'].unique()\n", + "funcao_bot" + ], "metadata": { - "id": "H-9ZYAcPvEn9" + "id": "gSt9oJ-Htnqj", + "outputId": "d6e2fa73-e844-4d13-df5c-b3b4cc55e744", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "outputs": [], - "source": [ - "scipy.stats.kruskal(res_geral, res_nao,res_sim)" + "execution_count": 191, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['não se aplica', 'Publicar hashtags', 'Compartilhar links',\n", + " 'publicar hashtags', 'Retweetar', 'compartilhar links', 'Postar',\n", + " 'Responder', 'compartilhar links ', 'Comentar', 'Atacar',\n", + " 'retweetar', 'atacar', 'Publicar imagens ou vídeos',\n", + " 'Mostrar Tweets apagados de atores políticos'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 191 + } ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LLDuwWGdvEn9" - }, - "outputs": [], - "source": [] } ], "metadata": { From 650c83cf519dfd5319246d473f2d420f7529383e Mon Sep 17 00:00:00 2001 From: Carla Oliveira Date: Sat, 3 Sep 2022 23:15:16 -0300 Subject: [PATCH 3/9] Criado usando o Colaboratory --- New_Model_From_INCT_DD_Evaluation.ipynb | 19841 ++++++++++++++++++++++ 1 file changed, 19841 insertions(+) create mode 100644 New_Model_From_INCT_DD_Evaluation.ipynb diff --git a/New_Model_From_INCT_DD_Evaluation.ipynb b/New_Model_From_INCT_DD_Evaluation.ipynb new file mode 100644 index 0000000..97745e5 --- /dev/null +++ b/New_Model_From_INCT_DD_Evaluation.ipynb @@ -0,0 +1,19841 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WdmimB43vEnb" + }, + "source": [ + "**Elaboração de um novo modelo de classificação com base nas informações de usuários avaliados pelo INCT-DD**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pfW-ynZ3vEne", + "outputId": "fe3d9ce9-3c96-4ea7-d6da-f53db36a3d11" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package rslp to /root/nltk_data...\n", + "[nltk_data] Unzipping stemmers/rslp.zip.\n", + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Unzipping corpora/stopwords.zip.\n" + ] + } + ], + "source": [ + "#Carrega as bibliotecas\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.tree import DecisionTreeClassifier \n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import train_test_split\n", + "from matplotlib import pyplot as plt\n", + "from sklearn import tree\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef, mean_squared_error, r2_score, mean_absolute_percentage_error, max_error, explained_variance_score, median_absolute_error\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neural_network import MLPClassifier, MLPRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import chi2\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.feature_extraction.text import TfidfTransformer\n", + "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, classification_report\n", + "import math\n", + "import statistics\n", + "import datetime\n", + "import pytz\n", + "import pickle\n", + "## NLTK (biblioteca para processamento de linguagem natural)\n", + "import nltk\n", + "nltk.download('rslp')\n", + "nltk.download('stopwords')\n", + "from nltk.stem.rslp import RSLPStemmer ##http://www.nltk.org/howto/portuguese_en.html\n", + "from nltk.corpus import stopwords\n", + "\n", + "#O primeiro uso exige obter os pacotes adicionais da biblioteca descomentando as linhas a seguir\n", + "#Instala os pacotes de termos do nltk (apenas na primeira vez)\n", + "#nltk.download()\n", + "#nltk.download('rslp')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EPYB_rxhvEng" + }, + "source": [ + "**O novo modelo de classificação de bots foi construído com base nos usuários manualmente avaliados pelo INCT-DD**\n", + "\n", + "Essa escolha foi tomada considerando que esse conjunto de dados é o melhor que se possui quanto à real possibilidade de um usuário do Twitter ser um bot, não existindo bases de avaliação dentro da realidade brasileira (especialmente quanto ao português), bem como atualizadas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461 + }, + "id": "OyGwd_QQvEnh", + "outputId": "792da5c4-24a0-452f-fafd-5cc52adbb5f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1074\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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1102019-03-09 11:29:52+00:00True04192.04886.0Maurcio989055950.0MG , BrasilMaurício Limahttp://pbs.twimg.com/profile_images/1104354755...1.104344e+18FalseFalse[]
2202009-10-20 01:19:19+00:00FalseFeliz é a Nação cujo Deus é o Senhor! #ReageBr...1341.01854.0LunViana0.0Araraquara, BrasilLucianahttp://pbs.twimg.com/profile_images/1436716357...8.373752e+07FalseFalse[]
3302020-05-03 19:06:46+00:00True02.031.0felipeleixas0.00Felipehttp://pbs.twimg.com/profile_images/1264366970...1.257024e+18FalseFalse[]
4402021-04-25 20:04:17+00:00True010.021.0JoseCar414511940.00Jose Carlos Marques de Albuquerquehttp://pbs.twimg.com/profile_images/1429559356...1.386411e+18FalseFalse[]
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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handle
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNlemathes
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNMaurcio98905595
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNLunViana
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonãoNaNfelipeleixas
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNJoseCar41451194
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtagstweet_idtweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_text
00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]1.501380e+1815013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtags...tweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_textretweet_tratadotweet_com_rt_tratadoretweet_e_tweet_com_rt_tratado
00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]...15013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!nãonãonão
11NaNlemathes52253248NaN2022-03-09 02:10:12+00:000.0FalseNaN[]...1501379796210757632FalseptNaNFalseTwitter for AndroidRT @LucianoHangBr: A vida precisa continuar e ...nãosimsim
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtags...tweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_textretweet_tratadotweet_com_rt_tratadoretweet_e_tweet_com_rt_tratado
11NaNlemathes52253248NaN2022-03-09 02:10:12+00:000.0FalseNaN[]...1501379796210757632FalseptNaNFalseTwitter for AndroidRT @LucianoHangBr: A vida precisa continuar e ...nãosimsim
55NaNlemathes52253248NaN2022-02-27 13:38:14+00:000.0FalseNaN[]...1497929065302482946FalseptNaNFalseTwitter for AndroidRT @roxmo: Puxa, que pena, passou tão perto!… ...nãosimsim
66NaNlemathes52253248NaN2022-02-18 04:17:53+00:000.0FalseNaN[]...1494526561902546944FalseptNaNFalseTwitter for AndroidRT @mila_sayuri: Alguém poderia confirmar se e...nãosimsim
77NaNlemathes52253248NaN2022-02-18 04:11:31+00:000.0FalseNaN[]...1494524957593845762FalseptNaNFalseTwitter for AndroidRT @RenzoGracieBJJ: Quando postei aqui o vídeo...nãosimsim
88NaNlemathes52253248NaN2022-02-18 04:10:00+00:000.0FalseNaN[]...1494524573919940609FalseptNaNFalseTwitter for AndroidRT @roxmo: Vc confia nas urnas eletrônicas?nãosimsim
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8240682406NaNFATIMAC758431781349784643244093440NaN2022-03-17 12:10:29+00:000.0FalseNaN[]...1504429966729138176FalseptNaNFalseTwitter for AndroidRT @EdmarVencedor: @BelaBonoro @OsvaldoLimaJni...nãosimsim
8240882408NaNFATIMAC758431781349784643244093440NaN2022-03-17 12:09:52+00:000.0FalseNaN[]...1504429810352898052FalseptNaNFalseTwitter for AndroidRT @BelaBonoro: @OsvaldoLimaJni1 @CeliaSLeao1 ...nãosimsim
8240982409NaNFATIMAC758431781349784643244093440NaN2022-03-17 12:09:18+00:000.0FalseNaN[]...1504429669613031426FalseptNaNFalseTwitter for AndroidRT @carlosjordy: Ciro sincero diz de quem é a ...nãosimsim
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16 - 1917\n", + "\n", + "Maurcio98905595 - 1 - 22\n", + "\n", + "LunViana - 2 - 34\n", + "\n", + "felipeleixas - 141 - 40791.0\n", + "\n", + "JoseCar41451194 - 9 - 584\n", + "\n", + "stefmilhori - 0 - 862\n", + "\n", + "Maurio0916 - 11 - 7975\n", + "\n", + "alaincremonezi - 7 - 210\n", + "\n", + "marctrickguedes - 24 - 436\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:28: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valdir_25 - 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1000\n", + "\n", + "SuperBolsomini1 - 100000 - 1000\n", + "\n", + "mfpecanha1 - 100000 - 1000\n", + "\n", + "arqueira_a - 100000 - 1000\n", + "\n", + "CludiaTanaka2 - 100000 - 1000\n", + "\n", + "Helena_Cabello1 - 2 - 11\n", + "\n", + "VeigaJuscelina - 100000 - 1000\n", + "\n", + "owoguinho - 100000 - 1000\n", + "\n", + "marilia_goretti - 0 - 21\n", + "\n", + "LuizAugustoPai4 - 6 - 38\n", + "\n", + "chocopoemlate16 - 1 - 16\n", + "\n", + "Joonbabykoya - 100000 - 1000\n", + "\n", + "zoldyevvil - 100000 - 1000\n", + "\n", + "predadoalfa - 6 - 219\n", + "\n", + "FePatriota1 - 3 - 19\n", + "\n", + "NandaAndretto - 100000 - 1000\n", + "\n", + "safetyjm - 100000 - 1000\n", + "\n", + "CarlosG82785363 - 1 - 60\n", + "\n", + "KP62A - 5 - 92\n", + "\n", + "marstwolf - 0 - 8123.0\n", + "\n", + "Marcos_11_66 - 0 - 37\n", + "\n", + "Rosiveti1 - 3 - 10\n", + "\n", + "uzusaske - 100000 - 1000\n", + "\n", + "vhsmessy - 100000 - 1000\n", + "\n", + "JMBBrasil - 100000 - 1000\n", + "\n", + "baia_canuto - 3 - 32\n", + "\n", + "pjmackerman - 4 - 16340\n", + "\n", + "EN30A - 100000 - 1000\n", + "\n", + "clara_kess - 3 - 94\n", + "\n", + "CesarNi85939384 - 3 - 10\n", + "\n", + "CHRBRYSHOR - 100000 - 1000\n", + "\n", + "PauloRo49195361 - 0 - 15\n", + "\n", + "AndrePenteado4 - 100000 - 1000\n", + "\n", + "Marina92011959 - 2 - 39\n", + "\n", + "Marcos_28_11_66 - 0 - 9\n", + "\n", + "bnqzyy_jkv - 100000 - 1000\n", + "\n", + "FATIMAC75843178 - 2 - 9\n", + "\n" + ] + } + ], + "source": [ + "#Incluir uma dedida da distancia temporal entre twittes (mediana e mínimo)\n", + "df_handles['Tempo mediano'] = np.array(len(df_handles))\n", + "df_handles['Tempo menor'] = np.array(len(df_handles))\n", + "iuser = 0\n", + "for user in df_handles['handle']:\n", + " df_temp = df_timeline[df_timeline['tweet_author'] == user]\n", + " itweet = 0\n", + " menor = 100000\n", + " difs = list()\n", + " tweet_date_prev = None\n", + " for tweet in df_temp['tweet_created_at']:\n", + " tweet_date = pd.to_datetime(pd.to_datetime(tweet).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"))\n", + " if itweet > 0:\n", + " dif = (tweet_date_prev - tweet_date).seconds\n", + " if dif < menor:\n", + " menor = dif\n", + " difs.append(dif)\n", + " else:\n", + " tweet_date_prev = tweet_date\n", + " tweet_date_prev = tweet_date\n", + " itweet += 1\n", + " if len(difs) > 0:\n", + " mediana = statistics.median(difs)\n", + " else:\n", + " mediana = 1000\n", + " print(user + ' - ' + str(menor) + ' - ' + str(mediana)+'\\n')\n", + " df_handles['Tempo mediano'][iuser] = mediana\n", + " df_handles['Tempo menor'][iuser] = menor\n", + " iuser += 1\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BG-iNlU3vEnq" + }, + "source": [ + "**Os dados inicialmente tratados são reunidos com a classificação dada pelo INCT-DD**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "id": "ppTFMTsTvEnq", + "outputId": "1b38577d-409d-45fa-b30b-0172311fcc6e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1072\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...followers_countfriends_countlanglocationnameprofile_imagetwitter_idtwitter_is_protectedverifiedwithheld_in_countries
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...21.0108.00.0Brasil, São PauloLeandro Matheshttp://pbs.twimg.com/profile_images/1141547105...52253248.00.00.0[]
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tweet_authortweet_text
0100_bolsonaro@OracoesB @wander_fabricio @DinhaCarvalho8 #Bo...
113valber1RT @leandroruschel: Tente encontrar na extrema...
21976MncRT @MinEconomia: “Nós estamos assistindo a uma...
3ACamargo241RT @juliovschneider: Se liga na viatura daqui ...
4AControldCarro Pajero TR4 4X4 Automatica, podendo sair ...
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tweet_authortweet_hashtags
0100_bolsonaro['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...
113valber1[], [], [], [], [], [], [], [], [], [], [], []...
21976Mnc[], [], [], [], [], [], ['PLP235NÃO'], [], ['P...
3ACamargo241[], [], [], [], [], [], [], [], [], [], [], []...
4AControld['RedeBBB', 'tbt', 'iphone', 'apple'], ['Natal...
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\n", + " " + ], + "text/plain": [ + " tweet_author tweet_hashtags\n", + "0 100_bolsonaro ['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...\n", + "1 13valber1 [], [], [], [], [], [], [], [], [], [], [], []...\n", + "2 1976Mnc [], [], [], [], [], [], ['PLP235NÃO'], [], ['P...\n", + "3 ACamargo241 [], [], [], [], [], [], [], [], [], [], [], []...\n", + "4 AControld ['RedeBBB', 'tbt', 'iphone', 'apple'], ['Natal..." + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Reune todos as hashtags utilizadas por um mesmo autor em um único texto, separando apenas por vírgula\n", + "df_result_hashtags = df_timeline.groupby('tweet_author').agg({'tweet_hashtags':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_hashtags.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "6LSMR2a_vEnr", + "outputId": "940e9ddf-5d55-4cbb-a47d-0b4637fffd34" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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tweet_authortweet_source
0100_bolsonaroTwitter Web App, Twitter Web App, Twitter Web ...
113valber1Twitter for Android, Twitter for Android, Twit...
21976MncTwitter for iPhone, Twitter for iPhone, Twitte...
3ACamargo241Twitter for Android, Twitter for Android, Twit...
4AControldTwitter Web App, Twitter Web App, Twitter Web ...
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tweet_authorretweet_tratado
0100_bolsonaronão, não, não, não, não, não, não, não, não, n...
113valber1não, não, não, não, não, não, não, não, não, n...
21976Mncnão, não, não, não, não, não, não, não, não, n...
3ACamargo241não, não, não, não, não, sim, não, não, não, n...
4AControldnão, não, não, não, não, não, não, não, não, n...
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tweet_authortweet_com_rt_tratado
0100_bolsonaronão, não, sim, não, não, sim, sim, sim, não, n...
113valber1sim, sim, sim, sim, não, não, não, não, não, n...
21976Mncsim, sim, não, não, sim, sim, não, sim, sim, s...
3ACamargo241sim, sim, sim, sim, sim, não, sim, sim, sim, s...
4AControldnão, não, não, não, não, não, não, não, não, n...
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\n", + "
\n", + " " + ], + "text/plain": [ + " tweet_author tweet_com_rt_tratado\n", + "0 100_bolsonaro não, não, sim, não, não, sim, sim, sim, não, n...\n", + "1 13valber1 sim, sim, sim, sim, não, não, não, não, não, n...\n", + "2 1976Mnc sim, sim, não, não, sim, sim, não, sim, sim, s...\n", + "3 ACamargo241 sim, sim, sim, sim, sim, não, sim, sim, sim, s...\n", + "4 AControld não, não, não, não, não, não, não, não, não, n..." + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Reune as informações de twettes com RT\n", + "df_result_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_tweet_com_rt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "zkPS0tjzvEns", + "outputId": "3357238f-5da8-4095-b6a2-24873d96256a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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tweet_authorretweet_e_tweet_com_rt_tratado
0100_bolsonaronão, não, sim, não, não, sim, sim, sim, não, n...
113valber1sim, sim, sim, sim, não, não, não, não, não, n...
21976Mncsim, sim, não, não, sim, sim, não, sim, sim, s...
3ACamargo241sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
4AControldnão, não, não, não, não, não, não, não, não, n...
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\n", + " " + ], + "text/plain": [ + " tweet_author retweet_e_tweet_com_rt_tratado\n", + "0 100_bolsonaro não, não, sim, não, não, sim, sim, sim, não, n...\n", + "1 13valber1 sim, sim, sim, sim, não, não, não, não, não, n...\n", + "2 1976Mnc sim, sim, não, não, sim, sim, não, sim, sim, s...\n", + "3 ACamargo241 sim, sim, sim, sim, sim, sim, sim, sim, sim, s...\n", + "4 AControld não, não, não, não, não, não, não, não, não, n..." + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Reune as informações da junção de retweets e tweets com rt\n", + "df_result_retweet_e_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'retweet_e_tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", + "df_result_retweet_e_tweet_com_rt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZwA3QA7dvEns", + "outputId": "940c2e50-12c5-4790-a949-ebbb613b9230" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", + " \n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", + " \n" + ] + } + ], + "source": [ + "#Reune os dados (merge) do usuários, suas avaliações com texto dos tweets, as hashtags, as fontes e os retweets\n", + "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_hashtags, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_source, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_retweet, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_retweet_e_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "DdtIwKDhvEnt", + "outputId": "b0871b69-9afa-4062-af03-325f72a059da" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "834\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
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Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "\n", + " Parece só Retweetar? ... tweet_author_y \\\n", + "0 não ... lemathes \n", + "\n", + " tweet_hashtags tweet_author_x \\\n", + "0 [], [], [], [], [], [], [], [], [], [], [], []... lemathes \n", + "\n", + " tweet_source tweet_author_y \\\n", + "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", + "\n", + " retweet_tratado tweet_author_x \\\n", + "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", + "\n", + " tweet_com_rt_tratado tweet_author_y \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "\n", + " retweet_e_tweet_com_rt_tratado \n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... \n", + "\n", + "[1 rows x 46 columns]" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exibe parte dos resultados da junção (nem todos os usuários ainda estão ativos e número de amostras diminui)\n", + "print(len(df_result_merge))\n", + "df_result_merge.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T4Eyp5jEvEnt" + }, + "source": [ + "**A classificação dos usuários foi padronizada para 0 - Não Bot e 1 - Bot**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-6hG03d0vEnt", + "outputId": "2e88723c-8756-4da9-d437-f489c5e6eee6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 0\n", + "Name: É Bot?, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", + "df = df_result_merge\n", + "y = df['É Bot?'].apply(lambda x: 1 if (x == 'Sim' or x == 'sim') else 0)\n", + "y.reset_index(drop=True, inplace=True)\n", + "y.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tsho3SNYvEnt" + }, + "outputs": [], + "source": [ + "##Seleciona as colunas para o conjunto X\n", + "#feature_cols = ['tweet_text'] #,'tweet_source','tweet_hashtags'\n", + "#x = df['tweet_text']\n", + "#x.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Ds8AtqBvEnt" + }, + "source": [ + "** [Classficando apenas pelo texto dos Twittes (NLTK)] **" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nVJ-KWXJvEnt" + }, + "outputs": [], + "source": [ + "##Prepara o conjunto de dados para treinamento e teste\n", + "#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ifa_JwZuvEnu" + }, + "outputs": [], + "source": [ + "##Método para vetorizar e contabilizar os termos\n", + "stemmer = nltk.stem.RSLPStemmer()\n", + "class StemmedCountVectorizerRSLPS(CountVectorizer):\n", + " def build_analyzer(self):\n", + " analyzer = super(StemmedCountVectorizerRSLPS, self).build_analyzer()\n", + " return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n", + "stemmed_count_vect = StemmedCountVectorizerRSLPS(stop_words=nltk.corpus.stopwords.words('portuguese'))\n", + "tfidf_transformer = TfidfTransformer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1BTPUUcsvEnu" + }, + "outputs": [], + "source": [ + "##Pipeline para extrair as informaçoes e classificar com base no texto (pode ser usado ANN ou MNB [MultinomialNB(fit_prior=False)])\n", + "#text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),\n", + "# ('tfidf', TfidfTransformer()),\n", + "# ('mnb', MLPClassifier(random_state=1, max_iter=600, activation='relu',solver='adam')),\n", + "#])\n", + "#text_mnb_stemmed = text_mnb_stemmed.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xyrFTNw-vEnu" + }, + "outputs": [], + "source": [ + "#text_mnb_stemmed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "teRgHViCvEnu" + }, + "outputs": [], + "source": [ + "##Avalia a classificação\n", + "#predicted_mnb_stemmed = text_mnb_stemmed.predict(x_test)\n", + "#np.mean(predicted_mnb_stemmed == y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PI4Z0JWlvEnu" + }, + "source": [ + "**Os atributos do treinamentos envolvem diversos fatores**\n", + "\n", + "Uma das etapas mais critícas da modelagem é a definição dos atributos que representam o cenário real, nesse sentido foram incluídas o máximo de variáveis que pudessem representar um usuário e suas atividades na rede, desde o tamanho do login escolhido até o tempo mínimo entre suas postagens. Na sequência são realizadas as atividades de extração, tratamento e junção dessas informações como atributos do conjunto de treinamento do modelo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BqT8a9b1vEnv", + "outputId": "3c89be92-85a1-4d8d-a351-ab4479a418e4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0_x', 'Unnamed: 0.1', 'tabelaAmostra', 'p', 'É Bot?',\n", + " 'Se você fosse atribuir uma função ao bot, qual seria?', 'Função #2',\n", + " 'Comportamento agressivo?',\n", + " 'Comportamento repetitivo com # ou menções?', 'Parece só Retweetar?',\n", + " 'Só compartilha links?', 'Só faz comentários?',\n", + " 'Enaltece muito outros usuários?', 'Faz muito uso de emojis?',\n", + " 'Tem muitos posts sem textos?', 'Unnamed: 14', 'handle',\n", + " 'Tempo mediano', 'Tempo menor', 'Unnamed: 0_y', 'error', 'created_at',\n", + " 'default_profile', 'description', 'followers_count', 'friends_count',\n", + " 'lang', 'location', 'name', 'profile_image', 'twitter_id',\n", + " 'twitter_is_protected', 'verified', 'withheld_in_countries',\n", + " 'tweet_author_x', 'tweet_text', 'tweet_author_y', 'tweet_hashtags',\n", + " 'tweet_author_x', 'tweet_source', 'tweet_author_y', 'retweet_tratado',\n", + " 'tweet_author_x', 'tweet_com_rt_tratado', 'tweet_author_y',\n", + " 'retweet_e_tweet_com_rt_tratado'],\n", + " dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns #df é o conjunto completo de dados, já com os twittes-hashtags-sources-retweets em campos únicos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "id": "NB5JSYG7vEnv", + "outputId": "b060a608-bef3-47ac-f0d6-40501815efe2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + " followers_count friends_count Tempo mediano Tempo menor \\\n", + "0 21.0 108.0 1917 16 \n", + "1 4192.0 4886.0 22 1 \n", + "2 1341.0 1854.0 34 2 \n", + "3 2.0 31.0 40791 141 \n", + "4 10.0 21.0 584 9 \n", + "\n", + " Quantidade hashtags Quantidade hashtags media Digitos no username \\\n", + "0 13 0.130000 0 \n", + "1 2 0.020000 8 \n", + "2 6 0.060000 0 \n", + "3 20 0.425532 0 \n", + "4 10 0.100000 8 \n", + "\n", + " Tamanho do username Tamanho do nome \n", + "0 8 14 \n", + "1 15 13 \n", + "2 8 7 \n", + "3 12 6 \n", + "4 15 34 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tjRssYzLvEnw" + }, + "source": [ + "A fonte do tweet foi considera importante informação, considerando que automações de postagens possam ser facilitadas a partir da versão Web ou que possa existir algum padrão no uso das diferentes fontes. Sendo assim, forneceu-se ao métodos a informação percentual da origem das postagens do mesmo usuário, seja Android, iPhone ou Web." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Y3HaOmS4vEnw" + }, + "outputs": [], + "source": [ + "#Calcula a quantidade de twittes por fontes\n", + "fonte_android = df['tweet_source'].apply(lambda x: str(x).count('Twitter for Android') )\n", + "fonte_iphone = df['tweet_source'].apply(lambda x: str(x).count('Twitter for iPhone') )\n", + "fonte_web = df['tweet_source'].apply(lambda x: str(x).count('Twitter Web App') )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eUSDhYGdvEnx" + }, + "outputs": [], + "source": [ + "fonte_soma = fonte_android + fonte_iphone + fonte_web\n", + "fonte_soma = fonte_soma.apply(lambda x: 1 if x <= 0 else x )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LQdbPscTvEnx" + }, + "outputs": [], + "source": [ + "#Calcula o percentual por usuário\n", + "fonte_android = fonte_android/fonte_soma\n", + "fonte_iphone = fonte_iphone/fonte_soma\n", + "fonte_web = fonte_web/fonte_soma" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 580 + }, + "id": "hfkQprbTvEnx", + "outputId": "ca9a2a12-1908-4431-cd52-a6afa3ee261b" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \n", + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Web
021.0108.0191716130.13000008141.000.000.00
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"x['Fonte de Web'] = np.array(list(fonte_web))\n", + "x = x.fillna(0)\n", + "x.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SYxSo6k5vEnx", + "outputId": "b7658620-fa83-48d6-811c-0eae01d46f05" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.641682\n", + "std 0.463189\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 1.000000\n", + "75% 1.000000\n", + "max 1.000000\n", + "Name: Fonte de Android, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Avaliação geral das diferentes fontes\n", + "x['Fonte de Android'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PTtW4jOvvEnx", + "outputId": "4d1d8d39-f65e-449b-be9c-36ae23cc676a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.198877\n", + "std 0.393385\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + "Name: Fonte de iPhone, dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x['Fonte de iPhone'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iIFeXnIQvEnx", + "outputId": "391d3b15-288b-46d5-c2f9-858bc6b5dd12" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 834.000000\n", + "mean 0.149848\n", + "std 0.330788\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 1.000000\n", + "Name: Fonte de Web, dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x['Fonte de Web'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jE-W1fivvEnx", + "outputId": "17339649-893c-4f66-c7d4-8de7cbe65488" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 não, não, não, não, não, não, não, não, não, n...\n", + "1 sim, sim, não, sim, sim, sim, sim, não, sim, s...\n", + "2 não, não, não, não, sim, não, não, não, não, n...\n", + "3 não, não, não, não, não, não, não, não, não, n...\n", + "4 não, não, não, não, não, não, não, não, não, n...\n", + "Name: retweet_tratado, dtype: object" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Inclui a informação do retweet\n", + "df['retweet_tratado'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7lcoFmwvvEny" + }, + "outputs": [], + "source": [ + "retweet_tratado = df['retweet_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['retweet_tratado_media'] = np.array(list(retweet_tratado))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1uXOoePGvEny" + }, + "outputs": [], + "source": [ + "tweet_com_rt = df['tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['tweet_com_rt_tratado_media'] = np.array(list(tweet_com_rt))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6KiugEWIvEny" + }, + "outputs": [], + "source": [ + "retweet_e_tweet_com_rt = df['retweet_e_tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", + "x['retweet_e_tweet_com_rt_tratado_media'] = np.array(list(retweet_e_tweet_com_rt))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OSyDe2swvEny" + }, + "outputs": [], + "source": [ + "x_novo = x" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZAQWrF-rvEny" + }, + "outputs": [], + "source": [ + "##Inclui os textos dos twittes (NLTK)\n", + "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", + "#tfidf_transformer = TfidfTransformer()\n", + "#x_tfidf = tfidf_transformer.fit_transform(st)\n", + "#x_tfidf\n", + "#x_novo = x.join(pd.DataFrame(x_tfidf.todense()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oYytkQlWvEny", + "outputId": "b80e141c-e46a-4e1f-b96a-8faa355b6651" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(834, 15)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_novo.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305 + }, + "id": "0Zs-qHPsvEnz", + "outputId": "5b42c4e5-c5ec-4609-b7a1-af27c9c16089" + }, + 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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Webretweet_tratado_mediatweet_com_rt_tratado_mediaretweet_e_tweet_com_rt_tratado_media
021.0108.0191716130.13000008141.000.000.000.100.7500000.840000
14192.04886.022120.020000815130.240.000.760.540.5200000.970000
21341.01854.034260.0600000870.180.820.000.080.8400000.910000
32.031.040791141200.42553201261.000.000.000.000.0425530.042553
410.021.05849100.100000815340.001.000.000.000.9400000.940000
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"3 0.042553 \n", + "4 0.940000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_novo.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ds5zbJqWvEnz" + }, + "source": [ + "**Com o primeiro conjunto de atributos formado é possível separar o conjunto de dados em treinamento e teste para a elaboração do modelo**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dZbiiGhAvEnz" + }, + "outputs": [], + "source": [ + "#Cria um modelo de classificação para o conjunto completo\n", + "x_train, x_test, y_train, y_test = train_test_split(x_novo, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zKaaJDpxvEnz", + "outputId": "4696dbcd-17a6-49f8-eddd-375e730f3522" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7330677290836654" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "np.mean(y_pred == y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a14V0FEnvEnz" + }, + "outputs": [], + "source": [ + "##Seleciona os atributos mais \"importantes\"\n", + "#x_new = SelectKBest(chi2, k=20).fit_transform(x_novo, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GLO1GeHovEn2" + }, + "outputs": [], + "source": [ + "#x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cuuGpOcdvEn3", + "outputId": "3d215a49-3720-4e80-e79d-6046044f3260" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.7330677290836654 | Balanced accuracy: 0.6958582834331337\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 49, 35],\n", + " [ 32, 135]])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "O5PS2y9hvEn3", + "outputId": "8a24eb8f-3b2e-4350-a1a2-ba3bd6ac6c6a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.60 0.58 0.59 84\n", + " 1 0.79 0.81 0.80 167\n", + "\n", + " accuracy 0.73 251\n", + " macro avg 0.70 0.70 0.70 251\n", + "weighted avg 0.73 0.73 0.73 251\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0y_Y_7uQvEn3", + "outputId": "6208c5ba-8f01-464a-ecd6-c3a8df824d2c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.7250996015936255 | Balanced accuracy: 0.6691616766467066\n" + ] + } + ], + "source": [ + "#Classificação com RNA\n", + "classifier = MLPClassifier(max_iter=1200, random_state=1, activation='tanh', solver='adam') #activation: logistic, relu, tanh, identity | solver: lbfgs, sgd, adam\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ki86QVDAvEn3" + }, + "source": [ + "**Informações de trend topics**\n", + "\n", + "Outra informação que se mostrou de relevância ao longo do trabalho de modelagem foi a relação das postagens de bots com as menções e hashtags listadas nos mais atuais 'trend topics', ou seja, o aparente uso de termos altamente utilizados no momento para possivelmente alavancar a visibilidade da postagem.\n", + "\n", + "Para averiguar essa possibilidade, um sistema de monitoramento dos tópicos mais mencionados foi criado e cada postagem coletada do usuário foi confrontado com os 'trend topics' do período mais próximo. Esse confrontamento gerou um percentual de uso desses tópicos nas postagens dos usuários." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 548 + }, + "id": "jnPs1tG6vEn3", + "outputId": "8bf209ff-69eb-4b45-86a1-fdb25d7ec3f2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2680\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-
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012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-2021-12-03
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-2021-12-03
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-2021-12-03
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-2021-12-03
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Jin, membro do BTS, está completando... 0 \n", + "3 - 0 \n", + "4 TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R... 0 \n", + "\n", + " tweet2 user3_id \\\n", + "0 - 0 \n", + "1 Como estão se sentindo com a nova indicação ao... 28431722 \n", + "2 - 0 \n", + "3 - 0 \n", + "4 - 0 \n", + "\n", + " tweet3 user4_id \\\n", + "0 - 0 \n", + "1 Vocês se preocupam com o futuro agora que já r... 78148969 \n", + "2 - 0 \n", + "3 - 0 \n", + "4 - 0 \n", + "\n", + " tweet4 user5_id tweet5 \\\n", + "0 - 0 - \n", + "1 OH Léo Dias eu vou mandar a fatura pra você, d... 0 - \n", + "2 - 0 - \n", + "3 - 0 - \n", + "4 - 0 - \n", + "\n", + " Trend Date Time Convertido \n", + "0 2021-12-03 \n", + "1 2021-12-03 \n", + "2 2021-12-03 \n", + "3 2021-12-03 \n", + "4 2021-12-03 " + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Inclui um percentual de trending topics utilizado por tweet\n", + "#Para tweet, busca pelos trending topics imediatamente anteriores\n", + "df_timeline['Numero de trendings'] = np.array(len(df_timeline))\n", + "df_timeline['Numero de trendings'] = 0\n", + "df_trends['Trend Date Time Convertido'] = np.array(len(df_trends))\n", + "\n", + "itrend = 0\n", + "for x in df_trends['trend_date_time']:\n", + " df_trends['Trend Date Time Convertido'][itrend] = pd.to_datetime(x).strftime(\"%Y-%m-%d\")\n", + " itrend += 1\n", + "\n", + "df_trends.head() " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wjdvEEvjvEn4" + }, + "source": [ + "O relacionamento dos trends e dos tweets foi realizado percorrendo todos os trends armazenados para cada tweet em data anterior ao do tweet e, para cada trend nessa condição, verificou-se no texto do tweet a presença de trendings. Caso esteja presente acumulou-se essa ocorrência, finalizando com a ocorrência de uso de uma trend por cada tweet.\n", + "Este trecho demanda de melhorias em desempenho e na inclusão de restrições que reduzam o tempo de ocorrência da trend para mais próximo do tweet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iR3IPD8jvEn4", + "outputId": "e3d89551-126a-499f-9847-b91dd19ca5b3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n", + "19\n", + "20\n", + "21\n", + "22\n", + "23\n", + "24\n", + "25\n", + "26\n", + "27\n", + "28\n", + "29\n", + "30\n", + "31\n", + "32\n", + "33\n", + "34\n", + "35\n", + "36\n", + "37\n", + "38\n", + "39\n", + "40\n", + "41\n", + "42\n", + "43\n", + 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+ " if trend_date <= tweet_date.tz_convert(None):\n", + " if df_timeline['tweet_text'][itweet].find(df_trends['trend'][itrend]) != -1: \n", + " df_timeline['Numero de trendings'][itweet] = df_timeline['Numero de trendings'][itweet] + 1\n", + " itrend += 1\n", + " print(itweet) \n", + " itweet += 1 " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IIb6GW_ZvEn4" + }, + "source": [ + "Para cada tweet foi armazenados o número de trend topics encontrado." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ulw5xkusvEn4", + "outputId": "8f1d9f5d-68fc-4733-bfa5-4384b1a45542" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 82413.000000\n", + "mean 0.001262\n", + "std 0.036843\n", + "min 0.000000\n", + "25% 0.000000\n", + "50% 0.000000\n", + "75% 0.000000\n", + "max 3.000000\n", + "Name: Numero de trendings, dtype: float64" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_timeline[df_timeline['Numero de trendings'] > 0].describe()\n", + "df_timeline['Numero de trendings'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XbYCwlmnvEn4", + "outputId": "c26440fb-41e6-4871-b9e0-103471bfae65" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(82413, 22)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_timeline.head(3)\n", + "df_timeline.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oBqtGHSevEn4" + }, + "source": [ + "As quantidades de trendings utilizadas em cada tweet foram agrupados por autor (usuário), assim foram incluídos na base de treinamento o número de trendings utilizadas, a média de trendings por tweet desse autor e o número máximo de trendings usado em um mesmo tweet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "VIZBn0qZvEn4", + "outputId": "c3b0bf5b-07c2-47d6-8339-f3942b7a122d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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tweet_authorNumero de trendingstrends_max
0100_bolsonaro00
113valber100
21976Mnc00
3ACamargo24100
4AControld33
............
830wolfjorge20100
831yoshio_carlos00
832zemariasccp100
833zeplu100
834zfabrogmailcom00
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835 rows × 3 columns

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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handleTempo medianoTempo menor
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNlemathes191716
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNMaurcio98905595221
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNLunViana342
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonãoNaNfelipeleixas40791141
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNJoseCar414511945849
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trending_idtrend_date_timetrenduser1_idtweet1user2_idtweet2user3_idtweet3user4_idtweet4user5_idtweet5Trend Date Time Convertido
012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-2021-12-03
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-2021-12-03
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-2021-12-03
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-2021-12-03
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595[], [], [], [], [], [], [], [], [], [], [], []...Maurcio98905595Twitter for Android, Twitter Web App, Twitter ...Maurcio98905595sim, sim, não, sim, sim, sim, sim, não, sim, s...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunViana[], [], [], [], [], [], [], [], [], [], [], []...LunVianaTwitter for iPhone, Twitter for Android, Twitt...LunViananão, não, não, não, sim, não, não, não, não, n...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixas[], ['EuApoioVotoImpresso'], [], ['GloboLixo']...felipeleixasTwitter for Android, Twitter for Android, Twit...felipeleixasnão, não, não, não, não, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194[], [], [], [], [], [], [], [], [], [], ['OsPi...JoseCar41451194Twitter for iPhone, Twitter for iPhone, Twitte...JoseCar41451194não, não, não, não, não, não, não, não, não, n...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes0.000.00lemathes00
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio9890559500
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...LunViana0.010.01LunViana11
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas00
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar414511940.000.00JoseCar4145119400
..................................................................
82910661067https://twitter.com/@CesarNi859393841111.csvSimRetweetarNaNnãosimsim...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi859393840.000.00CesarNi8593938400
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Se você fosse atribuir uma função ao bot, qual seria? \\\n", + "0 0000.csv não não se aplica \n", + "1 0000.csv não não se aplica \n", + "2 0000.csv não não se aplica \n", + "3 0000.csv sim Publicar hashtags \n", + "4 0000.csv Não não se aplica \n", + ".. ... ... ... \n", + "829 1111.csv Sim Retweetar \n", + "830 1111.csv Sim Retweetar \n", + "831 1111.csv Sim Retweetar \n", + "832 1111.csv Sim Retweetar \n", + "833 1111.csv Sim Retweetar \n", + "\n", + " Função #2 Comportamento agressivo? \\\n", + "0 NaN não \n", + "1 NaN não \n", + "2 NaN não \n", + "3 Atacar sim \n", + "4 NaN não \n", + ".. ... ... \n", + "829 NaN não \n", + "830 NaN não \n", + "831 NaN não \n", + "832 NaN não \n", + "833 NaN não \n", + "\n", + " Comportamento repetitivo com # ou menções? Parece só Retweetar? ... \\\n", + "0 não não ... \n", + "1 não não ... \n", + "2 não não ... \n", + "3 sim não ... \n", + "4 não não ... \n", + ".. ... ... ... \n", + "829 sim sim ... \n", + "830 sim sim ... \n", + "831 não sim ... \n", + "832 não não ... \n", + "833 sim sim ... \n", + "\n", + " tweet_author_x tweet_com_rt_tratado \\\n", + "0 lemathes não, sim, não, não, não, sim, sim, sim, sim, s... \n", + "1 Maurcio98905595 não, não, sim, não, não, sim, não, sim, não, n... \n", + "2 LunViana sim, sim, sim, sim, não, sim, sim, sim, sim, s... \n", + "3 felipeleixas não, não, não, não, sim, não, não, não, não, n... \n", + "4 JoseCar41451194 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + ".. ... ... \n", + "829 CesarNi85939384 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "830 PauloRo49195361 não, sim, sim, sim, não, sim, sim, sim, sim, s... \n", + "831 Marina92011959 não, não, não, não, não, sim, não, não, não, n... \n", + "832 Marcos_28_11_66 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "833 FATIMAC75843178 não, sim, sim, não, não, não, não, não, não, n... \n", + "\n", + " tweet_author_y retweet_e_tweet_com_rt_tratado \\\n", + "0 lemathes não, sim, não, não, não, sim, sim, sim, sim, s... \n", + "1 Maurcio98905595 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "2 LunViana sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "3 felipeleixas não, não, não, não, sim, não, não, não, não, n... \n", + "4 JoseCar41451194 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + ".. ... ... \n", + "829 CesarNi85939384 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "830 PauloRo49195361 não, sim, sim, sim, não, sim, sim, sim, sim, s... \n", + "831 Marina92011959 não, não, não, não, não, sim, não, não, não, n... \n", + "832 Marcos_28_11_66 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", + "833 FATIMAC75843178 não, sim, sim, não, não, não, não, não, não, n... \n", + "\n", + " tweet_author_x Numero de trendings_x trends_media tweet_author_y \\\n", + "0 lemathes 0.00 0.00 lemathes \n", + "1 Maurcio98905595 0.00 0.00 Maurcio98905595 \n", + "2 LunViana 0.01 0.01 LunViana \n", + "3 felipeleixas 0.00 0.00 felipeleixas \n", + "4 JoseCar41451194 0.00 0.00 JoseCar41451194 \n", + ".. ... ... ... ... \n", + "829 CesarNi85939384 0.00 0.00 CesarNi85939384 \n", + "830 PauloRo49195361 0.00 0.00 PauloRo49195361 \n", + "831 Marina92011959 0.00 0.00 Marina92011959 \n", + "832 Marcos_28_11_66 0.00 0.00 Marcos_28_11_66 \n", + "833 FATIMAC75843178 0.00 0.00 FATIMAC75843178 \n", + "\n", + " Numero de trendings_y trends_max \n", + "0 0 0 \n", + "1 0 0 \n", + "2 1 1 \n", + "3 0 0 \n", + "4 0 0 \n", + ".. ... ... \n", + "829 0 0 \n", + "830 0 0 \n", + "831 0 0 \n", + "832 0 0 \n", + "833 0 0 \n", + "\n", + "[834 rows x 52 columns]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_result_merge = pd.merge(df_result_merge,df_result_trend, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge = pd.merge(df_result_merge,df_result_trend_max, left_on=['handle'], right_on=['tweet_author'])\n", + "df_result_merge" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JZNurGgwvEn5", + "outputId": "e024f0f8-bba0-470c-e265-9412315b6d39" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20 - 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\" + str(iuser) + \" | \" + str((iuser/len(df_result_merge.tweet_text))*100) + \"%\")\n", + " df_result_merge['qtdtrends'][iuser] = ttemp\n", + " iuser = iuser + 1\n", + " ttemp = 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + }, + "id": "dR_UuC-lvEn5", + "outputId": "70a86ba4-3a26-4158-e53a-7909ece95b38" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...não, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes0.000.00lemathes008
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio989055950015
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...sim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...LunViana0.010.01LunViana118
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...não, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas0012
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar414511940.000.00JoseCar414511940015
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Webretweet_tratado_mediatweet_com_rt_tratado_mediaretweet_e_tweet_com_rt_tratado_mediaqtdtrendstrends_mediatrends_max
021.0108.0191716130.13000008141.000.000.000.100.7500000.84000080.000
14192.04886.022120.020000815130.240.000.760.540.5200000.970000150.000
21341.01854.034260.0600000870.180.820.000.080.8400000.91000080.011
32.031.040791141200.42553201261.000.000.000.000.0425530.042553120.000
410.021.05849100.100000815340.001.000.000.000.9400000.940000150.000
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\n", + "1 0.970000 15 0.00 0 \n", + "2 0.910000 8 0.01 1 \n", + "3 0.042553 12 0.00 0 \n", + "4 0.940000 15 0.00 0 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_novo_trend.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jrJgWnrJvEn6" + }, + "source": [ + "**Conjuntos de treinamento e teste**\n", + "\n", + "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r2Ydk5gJvEn6" + }, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xmo-1VWTvEn6", + "outputId": "e09bfe11-58e3-40ff-e69d-575c5a045a55" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.7410358565737052 | Balanced accuracy: 0.6929712004562304\n", + "Score: 0.7410358565737052\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 46, 38],\n", + " [ 27, 140]])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ySGFI_0UvEn6", + "outputId": "5c0d18c3-b7d6-438b-f24a-f7b00d5b8654" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean: 0.749003984063745 | Balanced accuracy: 0.6900841174793271\n", + "Score: 0.749003984063745\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 43, 41],\n", + " [ 22, 145]])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", + "classifier = classifier.fit(x_train,y_train)\n", + "y_pred = classifier.predict(x_test)\n", + "mean = np.mean(y_pred == y_test)\n", + "balanced = balanced_accuracy_score(y_test, y_pred)\n", + "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", + "confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "bg7cFJ8VvEn6", + "outputId": "fff005c6-51d0-4864-cfb5-732ef207f224" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "importances = classifier.feature_importances_\n", + "\n", + "indices = np.argsort(importances)\n", + "\n", + "fig, ax = plt.subplots(figsize =(10, 6))\n", + "ax.barh(range(len(importances)), importances[indices])\n", + "ax.set_yticks(range(len(importances)))\n", + "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kN336CjPvEn6" + }, + "source": [ + "**Resultados**\n", + "\n", + "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", + "\n", + "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", + "\n", + "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", + "\n", + "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MFWM1W5pvEn6" + }, + "outputs": [], + "source": [ + "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MMZ0DPRDvEn7" + }, + "outputs": [], + "source": [ + "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XPiyVsitvEn7" + }, + "outputs": [], + "source": [ + "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", + "#classifier = classifier.fit(x_train,y_train)\n", + "#y_pred = classifier.predict(x_test)\n", + "#mean = np.mean(y_pred == y_test)\n", + "#balanced = balanced_accuracy_score(y_test, y_pred)\n", + "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f4DmJ2b6vEn7" + }, + "outputs": [], + "source": [ + "#x_new_trend" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ijck93gzvEn7" + }, + "outputs": [], + "source": [ + "#confusion_matrix(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": 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If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n" + ] + }, + { + "data": { + "image/png": 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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handle
001https://twitter.com/@lemathes0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0lemathes
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0Maurcio98905595
223https://twitter.com/@LunViana0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0LunViana
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonão0felipeleixas
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplica0nãonãonãonãonãonãonãonão0JoseCar41451194
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\n", + " " + ], + "text/plain": [ + " Unnamed: 0 Unnamed: 0.1 tabelaAmostra p \\\n", + "0 0 1 https://twitter.com/@lemathes 0000.csv \n", + "1 1 2 https://twitter.com/@Maurcio98905595 0000.csv \n", + "2 2 3 https://twitter.com/@LunViana 0000.csv \n", + "3 3 4 https://twitter.com/@felipeleixas 0000.csv \n", + "4 4 5 https://twitter.com/@JoseCar41451194 0000.csv \n", + "\n", + " É Bot? Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não não se aplica 0 \n", + "1 não não se aplica 0 \n", + "2 não não se aplica 0 \n", + "3 sim Publicar hashtags Atacar \n", + "4 Não não se aplica 0 \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 sim sim \n", + "4 não não \n", + "\n", + " Parece só Retweetar? Só compartilha links? Só faz comentários? \\\n", + "0 não não não \n", + "1 não não não \n", + "2 não não não \n", + "3 não não não \n", + "4 não não não \n", + "\n", + " Enaltece muito outros usuários? Faz muito uso de emojis? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 não não \n", + "4 não não \n", + "\n", + " Tem muitos posts sem textos? Unnamed: 14 handle \n", + "0 não 0 lemathes \n", + "1 não 0 Maurcio98905595 \n", + "2 não 0 LunViana \n", + "3 não 0 felipeleixas \n", + "4 não 0 JoseCar41451194 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Lê os dados da aplicação do botometer\n", + "#Busca os dados dos usuários avaliados\n", + "datafile_botometer = \"/content/sample_data/handles_inct.csv\"\n", + "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", + "#Preenche os valores NaN con 0 apenas para avaliação geral\n", + "df_botometer = df_botometer.fillna(0)\n", + "print(len(df_botometer))\n", + "df_botometer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "id": "dREze2TlvEn9", + "outputId": "038a129e-2839-4009-e09d-062f956dedd5" + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Avalia os resultados do botometer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'não'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Não'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sim'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Sim'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" + \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3460\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'" + ] + } + ], + "source": [ + "#Avalia os resultados do botometer\n", + "a = len(df_botometer['analise_botometer'])\n", + "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", + "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", + "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", + "botometer_geral = df_botometer['analise_botometer']\n", + "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", + "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253 + }, + "id": "DzmZgqDkvEn9", + "outputId": "e8e8ddbf-28de-427e-a18d-314c97c0a804" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#(11, 6)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbplots\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbotometer_geral\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbotometer_nao\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbotometer_sim\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_geral\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_nao\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_sim\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'blue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightblue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightgreen'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pink'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbplot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbplots\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'botometer_geral' is not defined" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize =(20, 10)) #(11, 6)\n", + "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", + "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", + "c = 0\n", + "for i, bplot in enumerate(bplots['boxes']):\n", + " bplot.set(color=colors[c], linewidth=3)\n", + " c += 1\n", + " \n", + "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", + "c3 = 0\n", + "for cap in bplots['caps']:\n", + " cap.set(color=colorss[c3], linewidth=3)\n", + " c3 +=1\n", + "\n", + "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", + "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", + "plt.ylabel(\"Avaliação do Botometer\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mc5WWVrevEn9" + }, + "outputs": [], + "source": [ + "import scipy\n", + "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H-9ZYAcPvEn9" + }, + "outputs": [], + "source": [ + "scipy.stats.kruskal(res_geral, res_nao,res_sim)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_XSWWa_1lwQm" + }, + "source": [ + "

Análise de Sentimento

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Djeb7PUI77DC", + "outputId": "249f99d9-0620-4961-f224-ec93cf564d8b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n", + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (1.8.2.2)\n", + "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud) (1.21.6)\n", + "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud) (7.1.2)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from wordcloud) (3.2.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (1.4.4)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (2.8.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (3.0.9)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (4.1.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n" + ] + } + ], + "source": [ + "!pip install nltk\n", + "!pip install wordcloud" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "joxis7kss1II", + "outputId": "f4abf21f-d2a0-472b-e628-a3098cd93de0" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import string\n", + "import nltk\n", + "nltk.download('stopwords')\n", + "from nltk.corpus import stopwords\n", + "from nltk.probability import FreqDist\n", + "from wordcloud import WordCloud, STOPWORDS\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.feature_extraction.text import TfidfTransformer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LHgK1OAttT08", + "outputId": "4c1edee4-d3e6-4a14-c843-e67601cacdc3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 não\n", + "1 não\n", + "2 não\n", + "3 sim\n", + "4 não\n", + "Name: Comportamento agressivo?, dtype: object" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_handles['Comportamento agressivo?'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "UixBJa39kLDg", + "outputId": "225d4057-63aa-4ee6-fd69-daf5f84d961b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "834\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratadotweet_authortweet_text_y
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes@LucianoHangBr Já demorou muito!, RT @LucianoH...
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\n", + " " + ], + "text/plain": [ + " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", + "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", + "\n", + " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", + "0 não se aplica NaN \n", + "\n", + " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", + "0 não não \n", + "\n", + " Parece só Retweetar? ... tweet_author_x \\\n", + "0 não ... lemathes \n", + "\n", + " tweet_source tweet_author_y \\\n", + "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", + "\n", + " retweet_tratado tweet_author_x \\\n", + "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", + "\n", + " tweet_com_rt_tratado tweet_author_y \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "\n", + " retweet_e_tweet_com_rt_tratado tweet_author \\\n", + "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", + "\n", + " tweet_text_y \n", + "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... \n", + "\n", + "[1 rows x 48 columns]" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Seleção do texto e com o rótulo é agressivo ou não\n", + "df_result_merge_text = pd.merge(df_handles, df_users, on=['handle'])\n", + "df_result_merge_text = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", + "print(len(df_result_merge_text))\n", + "df_result_merge_text.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Fy96MPEvyXCc" + }, + "outputs": [], + "source": [ + "df_result_merge_text['Comportamento agressivo?'] = df_result_merge_text['Comportamento agressivo?'].str.lower()\n", + "#df_result_merge_text['tweet_text_y'] = df_result_merge_text['tweet_text_y'].str.lower()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3DxHVYbVpF6l" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l0iDtJtBns0Q", + "outputId": "ab71619b-21ec-4273-f6ba-3821eba54b7f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Dimensões:\n", + "\n", + "Shape: (834, 46)\n", + "\n", + "Quantidade de dados faltantes:\n", + "\n", + "Comportamento agressivo? 0\n", + "tweet_author 0\n", + "tweet_text_y 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "df_result_merge_text_analise=df_result_merge_text[['Comportamento agressivo?', 'tweet_author', 'tweet_text_y']]\n", + "print(\"\\nDimensões:\\n\")\n", + "print(\"Shape:\", df.shape)\n", + "print(\"\\nQuantidade de dados faltantes:\\n\")\n", + "print(df_result_merge_text_analise.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345 + }, + "id": "es3Nmym3vG7o", + "outputId": "990378b8-0d16-4989-c03d-f531ebf7c311" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tamanho dos comentários:\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes@LucianoHangBr Já demorou muito!, RT @LucianoH...10004
1nãoMaurcio98905595HOSPÍCIO....LOUCA. https://t.co/34BbY21hrQ, . ...7015
2nãoLunVianaRT @jairbolsonaro: - RIO DE JANEIRO / RJ: O @g...11420
3simfelipeleixas@RachelSherazade Vc chama isso de jornalismo? ...2846
4nãoJoseCar41451194RT @BrazilFight: JANAÍNA PASCHOAL\\n\"Jamais um ...11465
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\n", + " " + ], + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + "\n", + " tweet_text_y Tamanho \n", + "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... 10004 \n", + "1 HOSPÍCIO....LOUCA. https://t.co/34BbY21hrQ, . ... 7015 \n", + "2 RT @jairbolsonaro: - RIO DE JANEIRO / RJ: O @g... 11420 \n", + "3 @RachelSherazade Vc chama isso de jornalismo? ... 2846 \n", + "4 RT @BrazilFight: JANAÍNA PASCHOAL\\n\"Jamais um ... 11465 " + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_result_merge_text_analise['Tamanho'] = df_result_merge_text_analise['tweet_text_y'].apply(len)\n", + "print(\"Tamanho dos comentários:\\n\")\n", + "df_result_merge_text_analise.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "id": "ojUpwQTOvUFn", + "outputId": "9b1b3880-7003-44d3-9ccc-dda01016f064" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição do tamanho do texto')" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_dist_grafico = plt.figure(figsize=(10,6))\n", + "sns.distplot(df_result_merge_text_analise['Tamanho'], kde=True, bins=50, color=\"green\")\n", + "plt.title('Distribuição do tamanho do texto')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qu-5y7LFvou_", + "outputId": "050b7720-b280-4a8f-da63-c3aa8874048a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total: 834\n", + "Total de sim: 98\n", + "Total de não: 735\n" + ] + } + ], + "source": [ + "#total da likes\n", + "total = df_result_merge_text_analise['Comportamento agressivo?'].count()\n", + "total_sim = (df_result_merge_text_analise['Comportamento agressivo?']=='sim').sum()\n", + "total_nao = (df_result_merge_text_analise['Comportamento agressivo?']=='não').sum()\n", + "print(\"Total:\", total)\n", + "print(\"Total de sim:\", total_sim)\n", + "print(\"Total de não:\", total_nao)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aOkaDiM60gxw" + }, + "outputs": [], + "source": [ + "texto = df_result_merge_text_analise[['Comportamento agressivo?', 'tweet_author', 'tweet_text_y']]\n", + "texto['Tamanho'] = texto['tweet_text_y'].apply(len)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KG8nJnA80xhL" + }, + "outputs": [], + "source": [ + "stopWord = stopwords.words(\"portuguese\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XMAsFIAc09j3" + }, + "outputs": [], + "source": [ + "def remove_puntuacao_stopwords(texto):\n", + "\n", + " remove_puntacao = [word for word in texto.lower() if word not in string.punctuation]\n", + " remove_puntacao = ''.join(remove_puntacao)\n", + " return [word for word in remove_puntacao.split() if word not in stopWord]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "k0JVUzGr1Iv4", + "outputId": "c4ed3a64-b16c-41d7-db1e-9a14998f02a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tamanho dos comentários após aplicação do stopword:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
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\n", + " " + ], + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + "\n", + " tweet_text_y Tamanho \n", + "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", + "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", + "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", + "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 " + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "texto_preprocessado = texto.copy()\n", + "texto_preprocessado['tweet_text_y'] = texto['tweet_text_y'].apply(remove_puntuacao_stopwords)\n", + "texto_preprocessado['Tamanho'] = texto_preprocessado['tweet_text_y'].apply(len)\n", + "print(\"Tamanho dos comentários após aplicação do stopword:\\n\")\n", + "texto_preprocessado.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "id": "3TSR5jYI1XCy", + "outputId": "d1fa1480-e3ce-472e-f388-c689fe361aa6" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribuição do tamanho do texto após aplicação de STOPWORD')" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_dist_grafico_processado = plt.figure(figsize=(8,4))\n", + "sns.distplot(texto_preprocessado['Tamanho'], kde=True, bins=50, color=\"blue\")\n", + "plt.title('Distribuição do tamanho do texto após aplicação de STOPWORD')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dW1fnIfj1lsj" + }, + "outputs": [], + "source": [ + "def grafico_frequencia(data):\n", + " plt.figure(figsize=(10,5))\n", + " FreqDist(np.concatenate(data.tweet_text_y.reset_index(drop=True))).plot(25, cumulative=False, color=\"green\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "1A0s6yxv1wye", + "outputId": "9b082065-e27b-408f-f660-d5aa0c42dfec" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gráfico de frequência de Comportamento agressivo = sim:\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = sim:\\n\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='sim'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "xxyZtxgw2POm", + "outputId": "42edc55a-a03f-4e41-d85e-d19fd008b819" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gráfico de frequência de Comportamento agressivo = não:\n", + "\n" + ] + }, + { + "data": { + "image/png": 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nM6hPTVELnIiIiJRjSF2ocVP7x4DxwGSgbbDnmNk+ZnabmT1iZg+b2Qdj+RQzu9nMnoj3k2O5mdl3zGypmT1kZkcWxTozXv+EmZ1ZVN5iZovic75jZja8L7+ytJ2WiIiIlGNICZyZvRWYD7wFeCtwr5mdOsjTOoCPuPts4FjgXDObDXwCuMXdDwZuiccArwEOjrezgR/E154CfBY4Bjga+Gwh6YvXvK/oeXOH8vXkxeQxk2msa2Tjzo3s6NhR6eqIiIhIlRjqJIZPAUe5+5nufgYhkbpgoCe4+wp3vy8+3gw8CuwNvBG4PF52OfCm+PiNwBUe3ANMMrOZwKuBm919nbuvB24G5sZzE9z9Hnd3wpImhVhVoXg7LbXCiYiIyFBZyH0Guchskbu/sOi4DniwuGyQ5+8H3AkcBjzt7pNiuQHr3X2SmV0PfMnd74rnbgE+DhwPNLn7xbH8AmA7cHu8/pWx/OXAx9399SVe/2xCqx4zZ85smTdv3lCqXbZt27bR3Nw8pGtP//PpPLrxUX7+sp9z2OTDUok5VLUcM6u4ilmbMbOKq5i1GTOruIqZ/5ilzJkzZ6G7z+lzwt0HvQFfBW4C3h1vNwJfHuJzxwELgTfH4w29zq+P99cDxxWV3wLMAT4KfLqo/IJYNgf4U1H5y4HrB6tPS0uLZ621tXXI1772ytc6F+LXPnZtajGHqpZjZhVXMWszZlZxFbM2Y2YVVzHzH7MUoNVL5DMDdqGa2UFm9jJ3/xjwI+BF8XY38OPBskYzawT+F7jS3X8bi5+L3Z/E+1WxfDlQvKP7rFg2UPmsEuVVZc+xmokqIiIiwzPYGLhvAZsA3P237n6+u58P/C6e61fsHr0UeNTdv1F06jqgMJP0TODaovIz4mzUY4GN7r6C0PJ3splNjpMXTgZuiuc2mdmx8bXOKIpVNbrXgtMYOBERERmiwdaB28PdF/UudPdFcVzbQF4GnA4sMrMHYtl/AV8Crjazs4CnCLNaAW4AXgssBbYB/xZfa52ZXQQsiNd93t3XxcfnAD8HxhC6dW8cpE65072hvRbzFRERkSEaLIGbNMC5MQM90cNkhP7WZTupxPUOnNtPrMuAy0qUtxImRlQtbWgvIiIiwzVYF2qrmb2vd6GZvZcwMUES0nZaIiIiMlyDtcB9CPidmb2LXQnbHGAU8C9ZVqxWaDstERERGa4BEzh3fw54qZmdwK6uyv9z91szr1mN0HZaIiIiMlxD2sze3W8Dbsu4LjVpUtMkRtWPYnPbZra1b6O5MftFAUVERKS6DXUrLcmImakVTkRERIZFCVwOaByciIiIDIcSuBzQTFQREREZDiVwOaDttERERGQ4lMDlgLbTEhERkeFQApcDGgMnIiIiw6EELge6Z6FqDJyIiIgMgRK4HNCG9iIiIjIcSuByoDAGTl2oIiIiMhRK4HJAC/mKiIjIcCiBy4EJoyfQ1NDE1vatbGnbUunqiIiISM4pgcsBbaclIiIiw6EELie0lIiIiIgMlRK4nNB2WiIiIjJUSuByQttpiYiIyFApgcsJbaclIiIiQ6UELic0Bk5ERESGSglcTmg7LRERERkqJXA5oRY4ERERGSolcDmhWagiIiIyVErgcqJ7Q/stz+HuFa6NiIiI5JkSuJwYN2oczY3NbO/Yzua2zZWujoiIiOSYErgc0XZaIiIiMhRK4HJEExlERERkKJTA5YgmMoiIiMhQKIHLEW2nJSIiIkOhBC5HtJ2WiIiIDIUSuBzRGDgREREZCiVwOaLttERERGQolMDliFrgREREZCiUwOWIZqGKiIjIUCiBy5FCF+rKLSu1nZaIiIj0SwlcjowdNZZxo8bR1tnGxp0bK10dERERySklcDmj7bRERERkMErgckYTGURERGQwSuByRhMZREREZDBK4HJG22mJiIjIYJTA5Yy20xIREZHBKIHLGY2BExERkcEogcsZbaclIiIig1EClzNqgRMREZHBKIHLGc1CFRERkcEogcuZ4oV8tZ2WiIiIlKIELmfGNI5hwugJtHe1s37H+kpXR0RERHJICVwOaRyciIiIDEQJXA5pP1QREREZiBK4HNJEBhERERlIZgmcmV1mZqvMbHFR2RQzu9nMnoj3k2O5mdl3zGypmT1kZkcWPefMeP0TZnZmUXmLmS2Kz/mOmVlWX8tI03ZaIiIiMpAsW+B+DsztVfYJ4BZ3Pxi4JR4DvAY4ON7OBn4AIeEDPgscAxwNfLaQ9MVr3lf0vN6vVbW0nZaIiIgMJLMEzt3vBNb1Kn4jcHl8fDnwpqLyKzy4B5hkZjOBVwM3u/s6d18P3AzMjecmuPs9HtbauKIoVtXrnsSwVS1wIiIi0pdludaYme0HXO/uh8XjDe4+KT42YL27TzKz64Evuftd8dwtwMeB44Emd784ll8AbAduj9e/Mpa/HPi4u7++n3qcTWjZY+bMmS3z5s3L5Ost2LZtG83NzWU//87n7uT8Befz0ukv5TvHfCeVmKXUcsys4ipmbcbMKq5i1mbMrOIqZv5jljJnzpyF7j6nzwl3z+wG7AcsLjre0Ov8+nh/PXBcUfktwBzgo8Cni8oviGVzgD8Vlb+ckCgOWqeWlhbPWmtra6Lnz392vnMh/uIfvji1mKXUcsys4ipmbcbMKq5i1mbMrOIqZv5jlgK0eol8ZqRnoT4Xuz+J96ti+XJgn6LrZsWygcpnlSjfLWgWqoiIiAxkpBO464DCTNIzgWuLys+Is1GPBTa6+wrgJuBkM5scJy+cDNwUz20ys2NjV+wZRbGqXvE6cF3eVeHaiIiISN5kuYzI/wB3A4eY2bNmdhbwJeBVZvYE8Mp4DHADsAxYCvwEOAfA3dcBFwEL4u3zsYx4zU/jc54EbszqaxlpoxtGM6lpEp3eybrtveeBiIiISK1ryCqwu7+jn1MnlbjWgXP7iXMZcFmJ8lbgsCR1zLM9x+3Jhh0bWLllJdOap1W6OiIiIpIj2okhp7SdloiIiPRHCVxOaUN7ERER6Y8SuJzqboHTTFQRERHpRQlcTmk7LREREemPEric0nZaIiIi0h8lcDmlSQwiIiLSHyVwOaVJDCIiItIfJXA5pe20REREpD9K4HJqxtgZAKzauorOrs4K10ZERETyRAlcTo2qH8WUMVPo8i7Wbl9b6eqIiIhIjiiByzGNgxMREZFSlMDlmGaiioiISClK4HJMLXAiIiJSihK4HNN2WiIiIlKKErgcUwuciIiIlKIELse0FpyIiIiUogQuxzSJQUREREpRApdj6kIVERGRUpTA5Zi6UEVERKQUJXA5Nr15OoaxeutqOro6Kl0dERERyQklcDnWWN/I1OapOM6Gtg2Vro6IiIjkhBK4nCuMg1u7U/uhioiISKAELucKM1HX7VxX4ZqIiIhIXiiByzm1wImIiEhvSuByrtACpwRORERECpTA5Zxa4ERERKQ3JXA5V1gLTmPgREREpEAJXM4VWuDWtSmBExERkUAJXM4VxsCt2bmmwjURERGRvFACl3PqQhUREZHelMDl3PTm6dRZHRvaNnDRHRfxxyf/yPrt6ytdLREREamghkpXQAZWX1fP7OmzWbxqMZ+5/TPd5c+f+nyO3vtojt7raI6ZdQyH73E4oxtGV7CmIiIiMlKUwFWBO959Bz+8+YesHrWa+f+Yz30r7uPxtY/z+NrH+eVDvwRgVP0ojtjzCI7e62iO3jskdQdNOYg6UyOriIjI7kYJXBWYMmYKr9771bS0tADQ3tnOolWLmL98Pvcuv5f5y+fz6OpHmb98PvOXz4cF4XmTmiZx1F5Hcczex4TWur2P7h5TJyIiItVLCVwVaqxv5MiZR3LkzCP5jzn/AcDGHRtZuGJhd1J377P3smLLCm5edjM3L7u5+7n7Ttw3tNDtfQxjNo1h9KrRTG+eztTmqTTU6ddBRESkGug/9m5iYtNETtz/RE7c/8Tusmc3PdvdKnfv8ntp/UcrT218iqc2PsVvHvlNuOjecGcYU8ZMYfrY6cwYO4Ppzb3ux07v8XjqmKnU19VX4CsVERERJXC7sVkTZjFrwize/E9vBqCzq5PH1jzW3e264G8L2F63nVVbV7Fu+zrWbl/L2u1reWzNY4PGNoypzVP7JHttG9vYd+u+NNY10ljfyKj6Ud2PG+vicXw81PMb2zbS0dWhFkIREZFI/xFrSH1dPYfOOJRDZxzKe178HhYuXNg9rq6jq4N129exausqVm9dHe63re7xuLhs7fa1rNm2hjXbSiww/EQGlf8jjG0cy6SmSd23iU0Tw+PRRY+bJjFx9MS+1zRNoqmhKYOKiYiIjDwlcAJAQ10DM8bOYMbYGUO6vqOrg7Xb1vZJ9BYvW8z0PabT3tlOW2cb7V3ttHe2097Vz3F83O/1ne1sa9vG1o6tbG0Pt+Wbl5f1NY6uH92d0E0cPRHf6cx8fCZjR41lbONYxo0ax9jGsT2P4+Oxo0qfb2powszKqo+IiEi5lMBJWRrqGthj3B59ZrUubNjVqpeWhQsXcuSRR7KlbQsbdmxgw44NbNy5sfvxhh0b2Lij6Hhnr+N429m5k1VbV7Fq66pdwdcmq1ud1dHc2Nyd0E2wCRz+7OEcMOkADpgcbgdOOZDpzdOV6ImISGqUwElVMDPGjx7P+NHj2WfiPsN+vruzo2NHj4Tu/ofvZ6/99mJrW2zda9vKlrYt3Y8LLX5b2rb0e83Ozp1sadvClrYtPLf1OQDuX3d/n9cf2zi2O5nrndztO3FfLcIsIiLDogROaoKZMaZxDGMaxzBz/EwARq0aRcsLkrUWdnR1dCd3m3du5taFt9IwrYEn1z/JsvXLWLZ+GU+uf5INOzawaNUiFq1a1LduGLMmzAoJ3eQDu5O7QoLn7onqKCIiux8lcCIJNNQ1MLFpIhObJsJ42DJtS8ku5PXb1/dI6AqPl61fxtMbn+aZTc/wzKZnuOOpO/o8t446xv1pHONGjWP8qPGMGxUfj46PG4se93dNr3IREaluSuBERsDkMZNpGdNCy159k7v2znae3vh0vwnexp0b2bRzE5t2bkqtPobRcEMDjfWNNNQ19Lk11vVTPsD1GzdsZMbTM6i3ehrqGqi3eurrdj1uqGugvq6+x+PBrnvmmWdYXL+4b/1LjCc0So8x7H3tU8uf4unmp2lubO73NqZxjLahE5FcUwInUmGN9Y0cOOVADpxyYMnz9y64l0NeeEj3WLvNOzeH+7bNfcr6lJe4ZnPbZjq6OsKM3672dL+Yf6QbDoAHM4jZd5hiH00NTQMmec2NzTQ37Er4Vj23ir027IVh1FkdZvEe6/F4KOcKj5955hnmd83vLiu+L37+UO4Lsf++4u+sfHxlv+sxDrY2oxJbkXxQAieScw11Dd1r2aXB3VmwcAGHH3E4HV0dPW7tXe19yjq6Omjv7Ke86PonnnyC5+37PDq6Ouj0znDf1dnjceFc8eOBrlu1ZhVTp07tU/8+XxOlxwmWunb12tU0T2hmW/u2AW87Onawo2MH67avG/o398mhXzpkfRsgk1tY/lPrrb5P8tfV0UXjHY3drZ2FxLHwGOhOJEs9LvWcnTt3MuaeMX2S2oES3sES4y2btzB1ydTSyWtR0jqc+6dWPsU/lvwjlYS9cK7O6li6aSlj14ztfp3ilvHC6xdaq6U2KYETqTFmRr3VM7phNKNJb/brwh0LaTk8/SVksliWZrCYhVnLgyV5xbdnlj/DzJkzcRx3p8u7cOK9e4/HQz23avUqpk2b1n1N7/vCtf2dL74vXLt23VqaxzeXtSZje1d7SMA7OtnBjp7ftB2lv5eJbMkg5uoMYrZmEBPgzsEvMaxHgjdQsrdj+w6aW5sHTKaH+3jrlq1MfHhidxLa+1aclPY5189z1q5Zy54r9+wz5KIw3KL3sIve50pd//d//J0nm57s/p51f/+KhliUKh/o2qUrl3LA9gOYPGbyEH+g6VICJyLSS/Gs5alMHfwJVC7ZHMmY7k6nd/ZI6No623jgwQd44QtfGK6JCWPhceF5/T3u7zmLFi9i9uzZiZLg3tc99vhj7H/A/v0mqkO+L3r+mvVrmDhx4qCvPZxznd7Jlm1baBjV0N36XXjN4pbv9s52HKets422zrah/UZg4tcAACAASURBVBDTG0q7S8L1NEt6OoOY96Uf8uVHvJyj9z46/cBDoARORESGxMxosDBxhcZd5SvGrChrfcaBbB2/lUNnHJpqzKkbptJySH4S4jTiFoYc9E7wSiV7jzz6CC94wQuGlFgP9fGSJUs46OCDeiSkvW+FBLXkuV7P6ezq5O9P/Z2999m7e2hF7+EXxffFQzB6XO89y9euW8vkyZN7DLcoHmJRqnywazds2JDa0JZyKIETERGpUvV1oZtwKMMhuv7RVXImfBLj146n5YCUk2LLV8vzQDGfP/X5qcYcjqqfTmRmc81siZktNbNPVLo+IiIiIlmr6gTOzOqB7wOvAWYD7zCz2ZWtlYiIiEi2qjqBA44Glrr7MndvA64C3ljhOomIiIhkyqp5n0UzOxWY6+7vjcenA8e4+3m9rjsbOBtg5syZLfPmzcu0Xtu2baO5uVkxcxwzq7iKWZsxs4qrmLUZM6u4ipn/mKXMmTNnobvP6XPC3av2BpwK/LTo+HTgewM9p6WlxbPW2tqqmDmPmVVcxazNmFnFVczajJlVXMXMf8xSgFYvkc9UexfqcqB47vqsWCYiIiKy26r2BG4BcLCZ7W9mo4C3A9dVuE4iIiIimarqdeDcvcPMzgNuAuqBy9z94QpXS0RERCRTVZ3AAbj7DcANla6HiIiIyEip9i5UERERkZpT1cuIlMPMVgNPZfwy04A1ipnrmFnFVczajJlVXMWszZhZxVXM/McsZV93n967sOYSuJFgZq1eas0WxcxNzKziKmZtxswqrmLWZsys4ipm/mMOh7pQRURERKqMEjgRERGRKqMELhs/Vszcx8wqrmLWZsys4ipmbcbMKq5i5j/mkGkMnIiIiEiVUQuciIiISJVRAiciIiJSZZTAiYiIiFSZqt9KS4bHzEYBz4+HS9y9vZL1ERkJZnb+QOfd/RsjVRcRkTQogUuJmf3C3U8frKyMuKklXGZ2PHA58HfAgH3M7Ex3vzNJHbNgZs3AR4Dnufv7zOxg4BB3v77CVevDzJqAs4BDgaZCubu/J2Hcw4DZvWJekTDm6+hbz88niTkSzOwF7v5YghBzgKOA6+LxG4D5wBMJ6rQH8N/AXu7+GjObDbzE3S9NUM9MZPXBzcxm0PN36ek04qbNzE4BXhEP73D3eWXGucXdTzKzL7v7x9OrYXf8FwH7UfS/2d1/m1LsCb3irksh5mRgH3d/KGmsGG8M4T1/SRrxYsxxAO6+JaV4t7j7SYOVjQQlcOk5tPjAzOqBliQBM0i4vg6cXPjjMLPnA/9Tbj3N7LtAv9OY3f0D5cSNfgYsBF4Sj5cDvwGGncCZ2V3ufpyZbaZnfS1U0yckqCfAL4DHgFcDnwfeBTyaJKCZfRY4npDA3QC8BrgLKDuBM7MfAs3ACcBPgVMJSUySej4f+AGwh7sfFv8BneLuFyeJW8IfgecleP4s4Eh33wxgZhcC/+fupyWI+XPC7+mn4vHjwK+BshO4+EHli/RN3A9IEPN4Uv7gFhOirwN7AauAfQm/84cO9LxBYn4FuBjYDvwBeBHwYXf/ZbkxY9wvAkcDV8aiD5jZS9z9v8oIN9PMXgqcYmZXEb6f3dz9vgT1vIzwNT8MdBVCAokSODP7d+BzwA52vf85UNbvlJndDpxCyB8WAqvM7C/uPmAr9xDivgH4GjAK2N/MjgA+7+6nlBnvhYT3yynh0FYDZ7r74jLjNRHeP6fFxLXws58A7F1OzMTcXbcEN+CTwFagE9gUb5uBtcAXE8ZeSGh1Khw/H1iYIN5DQykbRrwzB7ol/Npb4/39RWUPVvrn3U9d7y/+XgKNwD0JYy4ijFF9MB7vAdycMOZDve7HAX9OGPMOwj/H4p/T4jJjfaef23eBTQnruQQYXXQ8mtASlSTmguKff3z8QMKYdwEnAQ8RkqILCf/EksRM9X0kxngQmFr0u38CcGnCmA/E+38hJMET0/ibj9/LuqLj+nLf9wgfem6M7/G3ArcV3W5NWM9Hkn6t/cR9ApiWYrzCz/y9wOcK3+MU4i6MP/Piv6dFCeL9FTih6Ph44K8J4n0Q+BuwE1hWdHsQOC+Ln91gN7XAJeTuXzSzLwFPuPtBKYdv9KKmZHd/3MwaE8RbaGY/BQqfaN8FtJYbzN0vT1CXwbTF5nQHMLMDCX84iWXQ7VPojtoQuz1XAjMSxtzu7l1m1hG7PlYB+ySNGe+3mdlehA8ZMxPGbHb3+WY9GiI6yoz1b4Ru81I/53eUGbPgCmC+mf0uHr+J0IKWxFYzm8qu39FjgY0JY45x91vMzNz9KeBCM1sIfCZBzLTfRwDa3X2tmdWZWZ2732Zm30oYs/D/6HXAb9x9Y6/fqyQmAYUuw4nlBnH3a4BrzOwCd78olZrtcreZzXb3R1KO+ySwLcV4DWY2E3gru1qf09Be4meeZKHase5+W3cg99vNbGy5wdz928C3zez9hFbC42L9/kzo0RhxSuBS4O5uZn8xs6PcfUGKoVNNuID/AM4FCl2bfwYuKTeYmX3L3T9kZvMo8YfmZTZ9RxcSulH2MbMrgZcR/sGXLYtun+jHsUn904QxVuOACxLGbDWzScBPCJ9MtwB3J4x5fYz5VeA+ws8s6RvPmphcF5KYU4EVZcZaQGi9+2vvE7HLs2zu/gUzuxF4eSz6N3e/P0lM4HzCz/tAM/sLMJ3QQpPETjOrA54ws/MIQwfGJYzZmvL7CIQPK+OAO4ErzWwVoSciievN7DHCB43/NLPphG6/pL4I3G9mtxG6vV4BfCJhzOOBHglcCuOgriAkcSsJH2IKQzxelCAmhF6iv5rZvRR9OPLyh7h8HrgJ+Iu7LzCzA0gwlrTIw2b2TqA+DiX4AKEVrVzLzOwCwhAXgNMILWZJvYLwQe078fidhJ/dW1OIPSzaiSEl8Y3nIOApwhtZ4j8+MxtNSLiOi0V/Bi5x92G3RMUxeQ+7+wvKrU+JmC3uvtDM/rnUeXe/I2H8qcCxhO/lPe6+JmG8B4ETgT+5+4vN7ATgNHc/K0HMOuBUd786Sd0GeY39gAme0kDhGHM00OTuiVqM4pv3j4GXAusJXQzviq1Hw401Bdjh7mm2FmTKzBqAQwi/o4knB5jZUYQPFZMICcIE4Kvufk+CmKm9jxTFHEtItOoICeFE4Ep3X1tuzBh3CrDR3TstTGSa4O4rk8SMcWcSJrEAzC83ZhwHNZbQfXo8PcdB/SHJ+6uZLSV8KFjErjFwlPO31CvufELXfO+4WfagDFv8eX8KOJnwfb0JuMjdy0ri44fqz9Hz9/5Cd1+fsJ6PuPvswcpGghK4lJjZvqXKy/3jyyjhuhZ4fwpdhgO9RiqzkrKY6WNmre4+JyZyL45dlA+6++EJ69rq7nOSxCiKdeRA572MQdJmdqK732pmb+4nZlmDpOPv6Jfd/aPxH3qdx0kCu7NBvp9O6Kq7y907hxm3+/uZRj2zEuv5J3c/IeW4jcB/UjRbFPhhCknxPOBXwHXunqiV0Mw+CHyI0Iq/nPhBnTAm7sfu/v0Ese9295cMfuWw497v7i9OMd4swrjUl8WiPwMfdPdn03qNPDOzXwLfK3yoMrNjgHPd/YyRrou6UFOS9FNSiXidZrbEzJ6XYsI1mdBMPZ+i7o6EXZ2pzkrKeKZPodvnz6TX7QPwJzP7KGEGYvH3tZxp+l8f4JwTWhCH6xWEFoM3UGIWLmXOcou/o8fFx4m/j2Z2tbu/1cwWlapnCl1Jaflndn0/S5lK6E5/1XCCFn8/02RmLyMMSdiXnstIlDULMdazy8wmJm3B7eUHhAlAhWEdp8ey9yaM+zXgbcCXzGwBcBVwfTktO0XjoD4DfMvdN8VuuiNJPsThfjP7FTCPnl2dSZcRudHMzi4Rt9xlRH5GSIjfEo9Pi2XD+n0v6G8ITsFw/z+lHa+EFkKXdOH/8vOAJYX3rZF8n1ILXI6Z2Z3AiwlLPSROuDLs6rw/dkm+l9D69lkze6icX+R+PuFCmN37E3f/XoJ6foowcH0l4U0nrW6fv1F6DGDZSz+kycw+QqifFd0TH+MJFrE1sx8QEuvf0PN3dNj/dMxspruvSLs1uxLM7NJyuubT/H4WxXwM+DDhw1V3q2CS3/vYmv9i4OZe9Sx76aBSreFptJAXxaonfAB6HzDXEywfVHh/iwn3RYQk8TPufkyCmD8rUeyefD3Jv/UTt9xlRB5w9yMGKxtGvML/pTcDe7JrrOY7gOfc/cOVjFcifsn3p4KRfJ9SC1y+JR0I30PSRG0Aqc1KKp7p4+7fTaV2uzQQ1hNbR2gt+3XS5C2aDZxDz1lJP0wS0MxKNsd7eQv5FgbBH0IYB3QtIYkrLGabRBNhNmtxy2BZrXruviLePxXfJA929z9ZmI2cu/cqM5sIfJaeXX6fd/eNCcZVpvb9LLLR3W9M8PxSfkvfOiVtDeg0swPd/UnoHl85rG7o/sTfoTcQWuKOJKyLl0ShXq8jfLD8PzNLtPahuyeapDVA3P1TDrnWzE4jrCEKITEq+3208H/JzL7eayjKPDMb9mSbtOOViJ+bD5JqgashFpY5+C7wT4Rp0PXA1iSfRGPctxCSzbvc/Zz4xvtVd//XhHFfSt9VyRPtRBDjvojwRv6vwLPu/sqE8a4mtBAWFgp9JzDR3cuelWRhkeSCJsLaYPe5e9mzHGOL7ut812K24wmL2b5i4GeOLDN7H3A2MMXdD7QwI+2HScY/ZsHM/hdYzK5k4HTgcHcvOdawUiwsc1RPSLiKu9CSLDr7wfhha8CyYcY8idAVt4zwAWNfwmzh2wZ84uBxryasVfgHwge3O9y9a+BnDRrzekIPwasICeF2wuSIYbcWmtn/c/evWD8Loydp1Yzxe48tvB34UbljC+OHq+8SFll3wkzRDyQd6mNmjxLen5bF4/2BG9z9n/IQL4+UwOVY2glX/PTxdkL3zBzgDOD57v7JdGqcHjP7BXAg8AC7Pu160jezGHtPwviNtwPjk45ZKDUDqVRZwteYBFzl7nMTxFgCvMjj7EMLsxMfcvdDEsQ8APg2YbawE8YBfcjdS3XbDDXmA4R/uPcWBl+b2SJ3f2G5MbOQdldSfH7qA8QtLJ/Rm7t7OeMpCzHvc/cje5UlHiwffycLv49LPMFM2aKYryZMukilNS/GbAbmEhaafSL2QLzQ3f9YRqw3uPs8Mzuz1HlPOFvUwhIyjfT8oNHp7knHFqbKzOYSZrQXJ/Bnl/M9HSDev7v7TenUuPJy1y0hPXyPEglXkoDuvtTM6uOb2c/M7H7COkFls2z2Ap0DzPYUP2GY2TmEbt7phO/p+zydRTPvM7NjveespMRN9b1sBZJ2hWSxmO2vgO8TVs+H8Pt6FVD2WCBgp7u3WVzQ08JSHXn8pLndzI5z97uge7LA9kGeM5i0B4jXE2ZffjNhvQrx3kFoYd7fzK4rOjWeXQvlDjdmfy2WB5lZGoP4bwXONbPUZrd6WOrmt0XHKyhz/UOP+7ImTdQGcFSvlsFbLczEL4uF9fneR9/ekURj9dz9D7G1vbDywmNJEvi04+WREricSznh2mZhU+sHLOw7uIKwjlNSqe8FSuia2pPyF4UtZR9C69ADKcaEDGYlWc+ZVHWEcXaJ1przbBazbXb3XxQd/9LMPpYw5h1m9l/AGDN7FWF8YVmbj2fsP4HL41g4COvglWxFGYbp7l48mP3nZvahcoN5mDH6DiCVBI7QXbYCmEbPGdObCVtWlaMwm3cGYT3BWwgtJifE10uawGU1uzVVMTH6OH33wS27pTRKe2zhtYSW4T8ljNND7Or9d4q6es0sSVdv73HEh8cPBImH4eSFulBzLI5ZeiVhtfyVhDfOd5c7KyuOXXiO0B37YcIszEvcfWnCehZmoRZmZjUS9tg8NkHM24AjCIPsi8ftJJ0CnrosZiVZzxnDHcBTSbrR0mZhwVUI/3DWE1rdnDC2cHKSbnkLiyOfRc8FPX+aZmtsGmJ336mErv5JhNXZ3d0/nyDmLYQWt+IB4v+WZPyfmX2TkMD0Xuam7DFwWTCzPxL2UF4Rj2cCP3f3VyeMm+ns1rTEr//XwEcJu+acCax2948njFs8thBCy1nZYwuTDhMYIG6qXb1ZjCPOGyVwOZZVwpU2M5vv7kfHhPMcQrI53xMso2EZLXki6bBdS6eU2qzSk/zsq4WZ/QHYQNiWrHh5joHW8hssZuoDxDMaA/dm4MuEVjOLNy93fG6M+WjxAPOYyD+cdNC5md0HvKVXC9Q1vcfwVZqZLXT3FitagsnMFrj7UYM9d5C4TYQ9hk8i/L4uAL7p5e9wcDFhU/gbktSrRNysl5FJPI44b5TA1RBLeUHPorjvBf4XeCFhTNU44AJ3/1HCuMVLSTQD9b6br/RvZpspPd4r8T/IamE5X1evwMwWu/thla5HJVjY9ukN7p50qERxzO8BB7Or9fFtwFJ3f3/CuKm2QGXFzO5x92PN7CbCPpv/ICSaByaMW2qW/CR3f0v/zxow3mbCdmI7gXZSem/KOtGOPUMPu3uiceR5ojFwOZZBwnUpJRb0TCJ+St7kYX+5O4FU/sla0VIShC6qvQlrq+VqKYm0ufv4StdhOCzlJQqi4rWbmggD+qf0c20l/dXMXujui9IKaGbfKVG8EWh192vLjLkH8N/AXu7+GjObDbzE3S9NUNXn0kzeANz9PDP7F3b9Lv3Y3X830HOG6C/Aj9jVAnUTyXdNyMLFcTzlRwitsBMI79dJHeY9Z8TfZmZlT95y9/FxCMXBFI3VS8HHCHXrkWiXG6zXJJtUxhHnjVrgcsxSXkHdzO71BCuFDxA3tb1Ai2JWxVIStW6kligodC+lGbNctmurrwbCP7FlhNaIxFt+mdmPCbPmfhOL/hX4G2F7rmXuPuwJDXHiys+AT7n74XFW7/1J/pbM7NuESUa/J8Vtn2KyeTTh+zvf3VcliRdjptoCVW0s5b07Y4/LB4FZhGWejiV0qSb6cJ1BV+98QlIIYRzx08B5SccU5ola4PIt7RXUbzOzr5Ligp5RmnuBFlTLUhK1LtUlCgDMrLjLpI7QIpen96rXZxj7RcDL4qxzLGyt9WfCLh/ltvRNc/erzeyTAO7eYWZJW+AnANsIE00KEu0YYWZvBb5KaMU14Ltm9jF3vyZBPSHlFqis2K41FV8CdBFaCT/scSHaMuIVPmg0smuWvBN6dB5LUNUPEnZ0ucfdTzCzFxBaeJO6gpBoXxSP30lY4aDcRLuh95hpM3sNYeLVbiFPb4rSV9oJV6H1rbi1rNwN0ou9LcY5p1d5ku7UallKotZlsf1R8SSADuDvhPX7cqGcWcXDMJkwhrSwSfxYwo4UnWZW7hpWW81sKvEDkIUFwhNtQu/ZbPv0KcIHglXQvazGn4CkCdxIrNOYhlJrKv4P5a+pmNUHjR3uvsPMMLPR7v6YmZW9GHiRVBJtM/tPwv+LA8yseGmb8YTu9N2GErh8SzXhcvcTEteotNT3AgU+QVhKYhFhbaAbCMupSL58lBTHrUCmv6fV4CuEdRpvJ7RCvQL4bzMbS0hmyvER4DrgQDP7C2Eh60RLKVgGO0YAdb26TNeSzjqVqa/TmJFU11TM8IPGs3FG5++Bm81sPZDGa6WVaP8KuBH4IuH/SMHmhL1CuaMxcDXEBth8O2Hc1PcClepgYR/cmwiJ25sI3T+fStItb2bnD3Te3b9RbuxqYGZ7EcYSPkpojXvW3e9MGLOBsEWVEbaoSjLJBDO7mfCPspBwnAa8y93L2jEixvwKcDg9Z6E+lMI6aKmv05gFM/syJdZUJHQrJx2Skom43NNE4A/u3pYw1qOE39EeiTahFT5PiXZuKIHLsbQTLsto821LcS/QonEbJemPOF9s1+LNxxHGrnwN+EySyTJm9ivCGJvCLLI3EBZ0fgLA3T+XrNb51c8A8bsTrtn2ECEp+HWhqzuFemaxD+yXgXsJLfkQWvWO3Z0GnQ8kLp/TH8/bMjppq5ZEO0+UwOVY2glXFm+6MUZqs5yK/ojPjffFn/Dd3T/R91lSKbZrF44vEjb2/pUl3NTcwoLQryus+Wdm44H/c/dXDPzM6hc/wBQGiB9RGCCe5ENW/Jt6W7x1ESYbXe3JFgfOYseI+3qv+WVFi9qKSE8aA5dvB7r7vxYdfy4ur1GuVDffzmKWU+FTlpm9qlcS8HELCz0qgcuX5Wb2I8Jm61+2sL1U0nFLewDF3TFtsawWpD5APP5NfQX4ioXNvS8g7KJQnyDsewhj4L7Jrh0j3l1OoFoadD4QM6sHXkffTeJ36yEDUj4lcPmWasJFz823DVhHmW+6UZbLKZiZvczd/xIPXko6A5olXW8F5gJfc/cNFvavTLqZ/RXAfDP7HeH39I2EHT5qQSYDxHu1wnUC/y9hyM8T9i1dH+NPIXSfv6eMWDUz6HwQ84AdhIlbXRWui1QBdaHmmJkdQeg+7ZFwuXvSdbYmALj7psSVzIiZtQCXsetrXw+8J4U166QKxLXgXk6c1ezu91e4SiMurQHiZnYvoZX8N4RxcGWtK9YrZp9u8qRd57VO3cUyXGqByzF3fwA4PK2Ey8w+SBi3shn4Sfwn+Ql3/2PiyqbM3RcSvvaJ8TjRTFmpOp2EVginRlsjei9CmsAZ7r4kpVgFdWY2uVcLnP6fJHOjmZ2cx/djySd1SeWYmX0wJm+bgW+Y2X1mdvJgzxvAe2ISeDJha57TgS+lUNXUmNlp8f78uJzEWcBZRceym4sfNK4EpgEzCOthJdrQvMatNLNvmFlrvH298MEoga8Dd5vZRWZ2EWEM3FeSV7Wm3QP8zsy2m9kmM9tsZrntJZHKUwKXb2knXBbvXwtc4e4PF5Xlxdh4P76fm+z+zgKOcffPuvtnCEtpvK/CdapmlxE+BL413jYRWuLL5u5XAG8Gnou3N/dahFaG7xuEdRSb3X2Cu4939wmVrpTkl5q8861PwmVmSRKuhWb2R2B/4JNxeYZcdU+5+4/i/W671pcMyui5HVcn+fugUU3Sns0OgLs/AuRuT9Eq9gyw2DUwXYZICVy+pZ1wnQUcASxz920W9kfMYk/DxOKq7BcTZt3+gbDJ94fd/ZcVrZiMhJ8B98ZZqBB2eLi0gvWpdmnPZpdsLANuN7Mb6bn3tZYRkZI0CzXHzKyOXQnXhphw7e3uDw3y1N5xjhzofB5ndhYWGDazfyEsV3I+cKe7H17hqskIiLOQu/fZrMVZqGkxs8MJS7MUxr2tJywBMqz3EcmWmX22VLl6I6Q/SuByKO2Ey8xuGzhc+dv0ZMXMFrv7YWb2U+Aad/+DmT2oBK52mNkMoKlwnGTngFpWNPlnXLzfAmwEFsaZ7iJShZTA5VA1JlxpM7MvEbrOtgNHA5OA65PssSnVwcxOIcxy3AtYRdjU+jF3P7SiFatScW/ZOYS9ZY3Qov0QYcX/37i7Zo9WkJl9y90/ZGbzKLEPtLufUoFqSRVQAldDzKyRsBtDYU/J24EfuXt7xSo1gLi21EZ37zSzscB4d19Z6XpJtszsQeBE4E9xn9UTgNPc/awKV60qxb1lX+vuW+LxOOD/CDtoLHT32ZWsX60zsxZ3X2hmHwUW9Do93t2vr0S9JP+0jEiOmVmjmX3AzK6Jt/NiElauHwAtwCXx1hLLcsfMmgn7IxbqtxehFUF2f+3uvpawWGydu9+GfvZJzKBoUDzQDuzh7tt7lUsFxEXLAd4JrHX3O+IiznsR9q0VKUmzUPPtB4QtcC6Jx6fHsveWGe+oXmPIbo2tHXn0M2Ah8NJ4vJywFZA+je7+NsRWojuBK81sFWHclpTnSsKs3mvj8RuAX8VWbS0Dkh+nAteY2TsJ28idQVgDVKQkJXD5lnbC1WlmB7r7kwBmdgA919vKkwPd/W1m9g6AuOyJ1gKrDQ8C24APA+8izJ4cN+AzpF/uflFcmqIwq/c/3L01Pn5Xhaolvbj7MjN7O/B74Gng5NhKKlKSErh8Szvh+hhwm5ktIwxm3pecrgMHtJnZGOKgXjM7EHX31IoT3L2LsObh5RA2+q5slapbTNhaB71QRpyZLaLn5IUpQD2h1RRtcC/9UQKXb8UJF4RZY2UnXO5+i5kdDBwSi5a4e16Tos8SFvDdx8yuJLQevLuiNZJMmdl/EsY9HtgrYRsP/KUytRLJ3OsrXQGpTpqFmmNm1gR8BDgJ2ECYofRNd9+RIN45wHGET3x/Bn5YbrysxAWMTwVuIeyDacA97r6mohWTTMUN1icDXwQ+UXRqs7uvq0ytRETySQlcjpnZ1YSNp6+MRe8EJrn7WxLE2wwUtqNKFC9LZtbq7pp5KCIiUoISuBwzs0d6r9FUqqxS8bIUF/JdA/wa2FooV0uMiIiIxsDl3X1mdqy73wNgZseQbCBy2vGy9DZCN+85vcoPqEBdREREckUJXA4VzUpqBP5qZk/H432Bxyodb4TMpsR4vYrWSEREJCfUhZpDZrbvQOfd/alKxhsJ/Yz/m+jub61crURERPJBCVyNMLN64GF3f0Gl6zIU1TReT0REZKRpL9Qa4e6dwBIze16l6zJE95nZsYWDnI/XExERGVFqgashZnYn8GJgPj1ndp5SsUr1w8weJSw4/HQseh6wBOgAXKuTi4hILdMkhtpyQaUrMAxzK10BERGRvFILnIiIiEiVUQtcDTCzu9z9ODPbTM9Nk43QHTmhQlUTERGRMqgFTkRERKTKqAWuBpnZDKCpcOzuTw9wuYiIiOSMlhGpIWZ2ipk9AfwNuAP4O3BjRSslIiIiw6YErrZcBBwLPO7u+wMnAfdUtkoiIiIyXErgaku7u68F6syszt1vA+ZUulIiIiIyPBoDV1s2mNk4wsbwV5rZKooW9BUREZHqoFmoNcTMmoEdhOVDTgMmAFe6+7qKVkxERESGRQlcDehnHTiL913AOuCr7n5JRSooIiIiw6IETjCzqcBf3f2QStdFREREBqcETgAws5nu8iXpAgAAAoZJREFUvqLS9RAREZHBKYETERERqTJaRkRERESkyiiBExEREakySuBEpCaZ2afM7GEze8jMHjCzYzJ8rdvNTItmi0hqtJCviNQcM3sJ8HrgSHffaWbTgFEVrpaIyJCpBU5EatFMYI277wRw9zXu/g8z+4yZLTCzxWb2YzMz6G5B+6aZtZrZo2Z2lJn91syeMLOL4zX7mdljZnZlvOaauHh2D2Z2spndbWb3mdlv4u4omNmXzOyR2CL4tRH8XohIFVICJyK16I/APmb2uJldYmb/HMu/5+5HufthwBhCK11Bm7vPAX4IXAucCxwGvDuupQhwCHCJu/8TsAk4p/hFY0vfp4FXuvuRQCtwfnz+vwCHuvuLgIsz+JpFZDeiBE5Eao67bwFagLOB1cCvzezdwAlmdq+ZLQJOBA4tetp18X4R8LC7r4gteMuAfeK5Z9z9L/HxL4Hjer30scBs4C9m9gBwJrAvsJGwzd2lZvZmYFtqX6yI7JY0Bk5EapK7dwK3A7fHhO3fgRcBc9z9GTO7EGgqesrOeN9V9LhwXHgv7b2wZu9jA25293f0ro+ZHQ2cBJwKnEdIIEVESlILnIjUHDM7xMwOLio6AlgSH6+J49JOLSP08+IECYB3Anf1On8P8DIzOyjWY6yZPT++3kR3vwH4MHB4Ga8tIjVELXAiUovGAd81s0lAB7CU0J26AVgMrAQWlBF3CXCumV0GPAL8oPiku6+OXbX/Y2ajY/Gngc3AtWbWRGilO7+M1xaRGqKttEREUmBm+wHXxwkQIiKZUheqiIiISJVRC5yIiIhIlVELnIiIiEiVUQInIiIiUmWUwImIiIhUGSVwIiIiIlVGCZyIiIhIlfn/l5X1Qvyvc6IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = não:\\n\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='não'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Uh33_5yC2gG_" + }, + "outputs": [], + "source": [ + "def nuvem_palavras(Agressivo):\n", + " Agressivotexto = ' '.join(texto[texto['Comportamento agressivo?']==Agressivo]['tweet_text_y'])\n", + " wordcloud = WordCloud(\n", + " width = 3000,\n", + " height = 2000,\n", + " background_color = 'black',\n", + " colormap=\"hsv\",\n", + " stopwords = STOPWORDS).generate(str(Agressivotexto))\n", + "\n", + " fig = plt.figure(\n", + " figsize = (8, 4),\n", + " facecolor = 'k',\n", + " edgecolor = 'k',)\n", + " plt.imshow(wordcloud, interpolation = 'bilinear')\n", + " plt.axis('off')\n", + " plt.tight_layout(pad=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + }, + "id": "lC93YnyQ3PhL", + "outputId": "657e4fd7-64c6-4c49-c838-af67503c771b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nuvem de palavras para agressivo sim:\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Nuvem de palavras para agressivo sim:\\n\")\n", + "nuvem_palavras('sim')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + }, + "id": "Y2swNWf13ngt", + "outputId": "4527b640-a194-4f6f-ff77-b62cc41bb160" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nuvem de palavras para agressivo não:\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
...............
829nãoCesarNi85939384[rt, claudeluca, alguém, notícia, vão, cassar,...1127
830nãoPauloRo49195361[dindorio, seguindo, patriota, sdv, fechadocom...732
831nãoMarina92011959[betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas...687
832nãoMarcos_28_11_66[rt, drbots2, justiça, condena, influenciador,...1154
833nãoFATIMAC75843178[camelojubeni, konigmachado, marcos281166, kon...958
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\n", + " " + ], + "text/plain": [ + " Comportamento agressivo? tweet_author \\\n", + "0 não lemathes \n", + "1 não Maurcio98905595 \n", + "2 não LunViana \n", + "3 sim felipeleixas \n", + "4 não JoseCar41451194 \n", + ".. ... ... \n", + "829 não CesarNi85939384 \n", + "830 não PauloRo49195361 \n", + "831 não Marina92011959 \n", + "832 não Marcos_28_11_66 \n", + "833 não FATIMAC75843178 \n", + "\n", + " tweet_text_y Tamanho \n", + "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", + "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", + "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", + "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 \n", + ".. ... ... \n", + "829 [rt, claudeluca, alguém, notícia, vão, cassar,... 1127 \n", + "830 [dindorio, seguindo, patriota, sdv, fechadocom... 732 \n", + "831 [betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas... 687 \n", + "832 [rt, drbots2, justiça, condena, influenciador,... 1154 \n", + "833 [camelojubeni, konigmachado, marcos281166, kon... 958 \n", + "\n", + "[834 rows x 4 columns]" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", + "texto_preprocessado" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LtWq7dXtLVFA", + "outputId": "7ddddd06-1941-4bc7-9441-4bf52b87615c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 [lucianohangbr, demorou, rt, lucianohangbr, vi...\n", + "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9...\n", + "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m...\n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo...\n", + "4 [rt, brazilfight, janaína, paschoal, jamais, b...\n", + "Name: tweet_text_y, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 177 + } + ], + "source": [ + "x = texto_preprocessado['tweet_text_y']\n", + "x.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eXJsyZoo4sVy", + "outputId": "440ecf57-dc4d-4ca1-8769-e3033b44f09a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 0\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 0\n", + "Name: Comportamento agressivo?, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 178 + } + ], + "source": [ + "y = texto_preprocessado['Comportamento agressivo?'].apply(lambda x: 1 if (x == 'sim') else 0)\n", + "y.reset_index(drop=True, inplace=True)\n", + "y.head()" + ] + }, + { + "cell_type": "code", + "source": [ + "vetorizar = CountVectorizer(analyzer=lambda x: x).fit(x)\n", + "x = vetorizar.transform(x)" + ], + "metadata": { + "id": "YeRqmaDVOqx0" + }, + "execution_count": 187, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Dimensões da matrix esparsa: \", x.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yxQ3plNqO46w", + "outputId": "f4ee742c-3842-4b9f-afe9-1bcae331c96e" + }, + "execution_count": 188, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dimensões da matrix esparsa: (834, 102627)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)" + ], + "metadata": { + "id": "_8gkt2hbPTU3" + }, + "execution_count": 202, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "mnb = MultinomialNB()\n", + "\n", + "mnb.fit(x_train,y_train)\n", + "predicao_mnb = mnb.predict(x_test)" + ], + "metadata": { + "id": "JNECf36HPVuX" + }, + "execution_count": 203, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Matriz de Confusão - Multinomial Naive Bayes:\\n\")\n", + "print(confusion_matrix(y_test,predicao_mnb))\n", + "print(\"\\nRelatório de Classificação:\",classification_report(y_test,predicao_mnb))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B-iHxTihQRwi", + "outputId": "c7570435-9171-4a03-a604-7d3ec1439779" + }, + "execution_count": 204, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão - Multinomial Naive Bayes:\n", + "\n", + "[[198 14]\n", + " [ 38 1]]\n", + "\n", + "Relatório de Classificação: precision recall f1-score support\n", + "\n", + " 0 0.84 0.93 0.88 212\n", + " 1 0.07 0.03 0.04 39\n", + "\n", + " accuracy 0.79 251\n", + " macro avg 0.45 0.48 0.46 251\n", + "weighted avg 0.72 0.79 0.75 251\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import itertools\n", + "def plot_confusion_matrix(cm, classes,\n", + " normalize=False,\n", + " title='Matriz de Confusão',\n", + " cmap=plt.cm.Blues):\n", + " \"\"\"\n", + " Esta função imprime e plota a matriz de confusão.\n", + " A normalização pode ser aplicada definindo `normalize = True`.\n", + " \"\"\"\n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + " print(\"Matriz de confusão normalizada\\n\")\n", + " else:\n", + " print('Matriz de confusão sem normalização\\n')\n", + "\n", + " print(cm)\n", + "\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + " tick_marks = np.arange(len(classes))\n", + " plt.xticks(tick_marks, classes, rotation=45)\n", + " plt.yticks(tick_marks, classes)\n", + "\n", + " fmt = '.2f' if normalize else 'd'\n", + " thresh = cm.max() / 2.\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " plt.text(j, i, format(cm[i, j], fmt),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label')" + ], + "metadata": { + "id": "_LBneh0MQbrz" + }, + "execution_count": 197, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculando a confusion matrix\n", + "matriz_confusao = confusion_matrix(y_test, predicao_mnb, labels=[0,1])\n", + "np.set_printoptions(precision=2)\n", + "# Imprimindo a matriz de confusão sem normalização\n", + "plt.figure()\n", + "plot_confusion_matrix(matriz_confusao, classes=['Não=0','Sim=1'],normalize= False, title='Confusion matrix')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380 + }, + "id": "aWQjHk2-QwGV", + "outputId": "015fd33e-ba99-4d7b-aeac-593c6a264186" + }, + "execution_count": 198, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de confusão sem normalização\n", + "\n", + "[[198 14]\n", + " [ 38 1]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T5zR-UdTtiFE" + }, + "source": [ + "

Classificação do tipo de bot

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gSt9oJ-Htnqj", + "outputId": "d6e2fa73-e844-4d13-df5c-b3b4cc55e744" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['não se aplica', 'Publicar hashtags', 'Compartilhar links',\n", + " 'publicar hashtags', 'Retweetar', 'compartilhar links', 'Postar',\n", + " 'Responder', 'compartilhar links ', 'Comentar', 'Atacar',\n", + " 'retweetar', 'atacar', 'Publicar imagens ou vídeos',\n", + " 'Mostrar Tweets apagados de atores políticos'], dtype=object)" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Lista as funções atribuídas ao bots\n", + "funcao_bot = df_handles['Se você fosse atribuir uma função ao bot, qual seria?'].unique()\n", + "funcao_bot" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 86916d337c1ce6ebf0664153e2fb30e1c272f0bf Mon Sep 17 00:00:00 2001 From: Carla Oliveira Date: Sat, 3 Sep 2022 23:16:39 -0300 Subject: [PATCH 4/9] Delete New_Model_From_INCT_DD_Evaluation.ipynb --- New_Model_From_INCT_DD_Evaluation.ipynb | 19841 ---------------------- 1 file changed, 19841 deletions(-) delete mode 100644 New_Model_From_INCT_DD_Evaluation.ipynb diff --git a/New_Model_From_INCT_DD_Evaluation.ipynb b/New_Model_From_INCT_DD_Evaluation.ipynb deleted file mode 100644 index 97745e5..0000000 --- a/New_Model_From_INCT_DD_Evaluation.ipynb +++ /dev/null @@ -1,19841 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WdmimB43vEnb" - }, - "source": [ - "**Elaboração de um novo modelo de classificação com base nas informações de usuários avaliados pelo INCT-DD**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pfW-ynZ3vEne", - "outputId": "fe3d9ce9-3c96-4ea7-d6da-f53db36a3d11" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package rslp to /root/nltk_data...\n", - "[nltk_data] Unzipping stemmers/rslp.zip.\n", - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Unzipping corpora/stopwords.zip.\n" - ] - } - ], - "source": [ - "#Carrega as bibliotecas\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.tree import DecisionTreeClassifier \n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.model_selection import train_test_split\n", - "from matplotlib import pyplot as plt\n", - "from sklearn import tree\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, matthews_corrcoef, mean_squared_error, r2_score, mean_absolute_percentage_error, max_error, explained_variance_score, median_absolute_error\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPClassifier, MLPRegressor\n", - "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingClassifier\n", - "from sklearn.feature_selection import SelectKBest\n", - "from sklearn.feature_selection import chi2\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.feature_extraction.text import CountVectorizer\n", - "from sklearn.feature_extraction.text import TfidfTransformer\n", - "from sklearn.metrics import balanced_accuracy_score, confusion_matrix, classification_report\n", - "import math\n", - "import statistics\n", - "import datetime\n", - "import pytz\n", - "import pickle\n", - "## NLTK (biblioteca para processamento de linguagem natural)\n", - "import nltk\n", - "nltk.download('rslp')\n", - "nltk.download('stopwords')\n", - "from nltk.stem.rslp import RSLPStemmer ##http://www.nltk.org/howto/portuguese_en.html\n", - "from nltk.corpus import stopwords\n", - "\n", - "#O primeiro uso exige obter os pacotes adicionais da biblioteca descomentando as linhas a seguir\n", - "#Instala os pacotes de termos do nltk (apenas na primeira vez)\n", - "#nltk.download()\n", - "#nltk.download('rslp')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EPYB_rxhvEng" - }, - "source": [ - "**O novo modelo de classificação de bots foi construído com base nos usuários manualmente avaliados pelo INCT-DD**\n", - "\n", - "Essa escolha foi tomada considerando que esse conjunto de dados é o melhor que se possui quanto à real possibilidade de um usuário do Twitter ser um bot, não existindo bases de avaliação dentro da realidade brasileira (especialmente quanto ao português), bem como atualizadas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 461 - }, - "id": "OyGwd_QQvEnh", - "outputId": "792da5c4-24a0-452f-fafd-5cc52adbb5f8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1074\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0errorcreated_atdefault_profiledescriptionfollowers_countfriends_counthandlelanglocationnameprofile_imagetwitter_idtwitter_is_protectedverifiedwithheld_in_countries
0002009-06-30 01:05:51+00:001.0021.0108.0lemathes0.0Brasil, São PauloLeandro Matheshttp://pbs.twimg.com/profile_images/1141547105...5.225325e+070.00.0[]
1102019-03-09 11:29:52+00:00True04192.04886.0Maurcio989055950.0MG , BrasilMaurício Limahttp://pbs.twimg.com/profile_images/1104354755...1.104344e+18FalseFalse[]
2202009-10-20 01:19:19+00:00FalseFeliz é a Nação cujo Deus é o Senhor! #ReageBr...1341.01854.0LunViana0.0Araraquara, BrasilLucianahttp://pbs.twimg.com/profile_images/1436716357...8.373752e+07FalseFalse[]
3302020-05-03 19:06:46+00:00True02.031.0felipeleixas0.00Felipehttp://pbs.twimg.com/profile_images/1264366970...1.257024e+18FalseFalse[]
4402021-04-25 20:04:17+00:00True010.021.0JoseCar414511940.00Jose Carlos Marques de Albuquerquehttp://pbs.twimg.com/profile_images/1429559356...1.386411e+18FalseFalse[]
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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handle
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNlemathes
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNMaurcio98905595
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNLunViana
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonãoNaNfelipeleixas
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNJoseCar41451194
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtagstweet_idtweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_text
00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]1.501380e+1815013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!
\n", - "
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\n", - " " - ], - "text/plain": [ - " Unnamed: 0 error tweet_author tweet_author_id_str tweet_contributors \\\n", - "0 0 NaN lemathes 52253248 NaN \n", - "\n", - " tweet_created_at tweet_favorite_count tweet_favorited tweet_geo \\\n", - "0 2022-03-09 02:10:58+00:00 0.0 0.0 NaN \n", - "\n", - " tweet_hashtags tweet_id tweet_id_str tweet_is_retweet \\\n", - "0 [] 1.501380e+18 1501379987747876874 0.0 \n", - "\n", - " tweet_lang tweet_place tweet_retweeted tweet_source \\\n", - "0 pt NaN 0.0 Twitter for Android \n", - "\n", - " tweet_text \n", - "0 @LucianoHangBr Já demorou muito! " - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Recupera os últimos twittes\n", - "datafile_timeline = \"/content/sample_data/inct_timelines.csv\"\n", - "df_timeline = pd.read_csv(datafile_timeline, header = 0)\n", - "print(len(df_timeline))\n", - "df_timeline.head(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BnnbMc0jvEnm" - }, - "source": [ - "Aplica um pré-processamento nos dados para unificar a informação da postagens se tratar de um retweet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MxTnMw6evEnm", - "outputId": "1c982aa5-0a9f-4b24-d149-e3bf27e4fde8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['0.0', 'False', 'True', False, True], dtype=object)" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#identifica os formatos existentes\n", - "df_timeline['tweet_is_retweet'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6kAf3fAFvEnn", - "outputId": "0a64a24b-b098-44ce-84d1-a9ee6c453aeb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['não', 'sim'], dtype=object)" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_timeline['retweet_tratado'] = df_timeline['tweet_is_retweet'].apply(lambda x: \"sim\" if (x == 'True' or x == True) else \"não\")\n", - "df_timeline['retweet_tratado'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KRPYHx4-vEnn", - "outputId": "e391a395-1283-44e7-eab7-9e0fba9b460c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 não\n", - "1 sim\n", - "2 não\n", - "3 não\n", - "4 não\n", - " ... \n", - "82408 sim\n", - "82409 sim\n", - "82410 sim\n", - "82411 sim\n", - "82412 não\n", - "Name: tweet_com_rt_tratado, Length: 82413, dtype: object" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Necessário reverificar no texto do tweet por RT @, pois o campo tweet_is_retweet falha em algumas situações não identificadas\n", - "#Parecem ser os RT com comentários adicionais\n", - "#for tweet in df_timeline['retweet_tratado', 'tweet_text']:\n", - "# if tweet['retweet_tratado'] == 'não':\n", - "# if tweet['tweet_text'].find(\"RT @\") != -1:\n", - "# tweet['retweet_tratado'] = 'sim'\n", - "#len(df_timeline)\n", - "#for i in range(len(df_timeline)):\n", - "# if df_timeline.iloc[i]['retweet_tratado'] == 'não':\n", - "# if df_timeline.iloc[i]['tweet_text'].find(\"RT @\") != -1:\n", - "# df_timeline.iloc[i]['retweet_tratado'] = 'sim'\n", - "df_timeline['tweet_com_rt_tratado'] = df_timeline['tweet_text'].apply(lambda x: \"sim\" if x.find(\"RT @\") != -1 else \"não\" )\n", - "df_timeline['tweet_com_rt_tratado']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 310 - }, - "id": "L7lkmE_yvEno", - "outputId": "7d33ef35-9fdd-4457-caa0-7c149db29bee" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0errortweet_authortweet_author_id_strtweet_contributorstweet_created_attweet_favorite_counttweet_favoritedtweet_geotweet_hashtags...tweet_id_strtweet_is_retweettweet_langtweet_placetweet_retweetedtweet_sourcetweet_textretweet_tratadotweet_com_rt_tratadoretweet_e_tweet_com_rt_tratado
00NaNlemathes52253248NaN2022-03-09 02:10:58+00:000.00.0NaN[]...15013799877478768740.0ptNaN0.0Twitter for Android@LucianoHangBr Já demorou muito!nãonãonão
11NaNlemathes52253248NaN2022-03-09 02:10:12+00:000.0FalseNaN[]...1501379796210757632FalseptNaNFalseTwitter for AndroidRT @LucianoHangBr: A vida precisa continuar e ...nãosimsim
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8 - 41\n", - "\n", - "Plato14181684 - 2 - 16\n", - "\n", - "EuCarlosCrvg - 100000 - 1000\n", - "\n", - "NinaLuz23695256 - 5 - 26\n", - "\n", - "scris20231 - 10 - 41\n", - "\n", - "MarciaB16982788 - 0 - 19\n", - "\n", - "LucianeLazzarin - 5 - 20\n", - "\n", - "MargaretteBras5 - 100000 - 1000\n", - "\n", - "JBOlive31644311 - 2 - 21\n", - "\n", - "LiliaRRS8 - 2 - 21\n", - "\n", - "Camilo20211 - 2 - 20\n", - "\n", - "Roberso98250940 - 6 - 15\n", - "\n", - "lu_salvucci - 2 - 56\n", - "\n", - "ValmorRodrigu17 - 0 - 11\n", - "\n", - "Manuela42572532 - 4 - 38\n", - "\n", - "PauloAr90832347 - 6 - 84\n", - "\n", - "MariaRobertaAl8 - 5 - 41\n", - "\n", - "AnaSilviaBotti1 - 9 - 106\n", - "\n", - "Marly53440332 - 3 - 19\n", - "\n", - "ninalovemetal - 3 - 31\n", - "\n", - "Luka10871610 - 7 - 82\n", - "\n", - "AnaBeat34202412 - 1 - 135\n", - "\n", - "doragouvea - 2 - 52\n", - "\n", - "ganowicz_gan - 100000 - 1000\n", - "\n", - "itsjeonjkboy - 100000 - 1000\n", - "\n", - "Sidnei72007866 - 3 - 31\n", - "\n", - "AiltonAlvesBom2 - 2 - 35\n", - "\n", - "NevesJuvenil - 4 - 11\n", - "\n", - "FredericoFDias2 - 2 - 45\n", - "\n", - "JubVasconcelos - 3 - 26\n", - "\n", - "Anselmo04800217 - 100000 - 1000\n", - "\n", - "jeremiasalecri1 - 100000 - 1000\n", - "\n", - "Juracimoreira2 - 2 - 96\n", - "\n", - "zfabrogmailcom - 1 - 40\n", - "\n", - "LuizEdu29812978 - 1 - 35\n", - "\n", - "g_garc2 - 0 - 13\n", - "\n", - "RogrioG79108167 - 3 - 28\n", - "\n", - "DaviSil46494090 - 3 - 24\n", - "\n", - "lucia98624147 - 0 - 32\n", - "\n", - "MDSouza16 - 3 - 41\n", - "\n", - "silvano34982713 - 9 - 77\n", - "\n", - "NusaAlex - 5 - 72\n", - "\n", - "ParaibanoJorge - 100000 - 1000\n", - "\n", - "JairoPatriotaMG - 100000 - 1000\n", - "\n", - "MarionCobret2 - 100000 - 1000\n", - "\n", - "AVERYF4LLS - 100000 - 1000\n", - "\n", - "HugoTdeSouzaJn1 - 2 - 13\n", - "\n", - "DelfrariVinny - 5 - 30\n", - "\n", - "LucineaMariaDe1 - 0 - 16\n", - "\n", - "2Rockkk - 100000 - 1000\n", - "\n", - "Jos17846367 - 4 - 72\n", - "\n", - "Geanesa64267041 - 4 - 36\n", - "\n", - "Beto1967B - 2 - 67\n", - "\n", - "ManoelFidelis1 - 3 - 23\n", - "\n", - "ElacheElache - 8 - 61\n", - "\n", - "ROBSONB93874205 - 0 - 19\n", - "\n", - "Lilian14876478 - 0 - 15\n", - "\n", - "Geraldo35987490 - 3 - 13\n", - "\n", - "MarizMarcella - 0 - 40\n", - "\n", - "SaG9A - 100000 - 1000\n", - "\n", - "Josbrsousa - 2 - 9\n", - "\n", - "aragonez_pedro - 3 - 94\n", - "\n", - "Direito31585503 - 100000 - 1000\n", - "\n", - "IsmeniaFranco - 2 - 19\n", - "\n", - "MarcosA14278872 - 2 - 26\n", - "\n", - "RelredeS - 0 - 14\n", - "\n", - "CPER1972 - 100000 - 1000\n", - "\n", - "GersonC33316796 - 3 - 18\n", - "\n", - "ChobasB - 4 - 22\n", - "\n", - "Belfav - 0 - 23\n", - "\n", - "CruzAdrianai3 - 100000 - 1000\n", - "\n", - "sales_amaral - 5 - 19\n", - "\n", - "___DENISE___EU_ - 4 - 16\n", - "\n", - "MauroAlvesZL - 100000 - 1000\n", - "\n", - "mariasansone161 - 3 - 12\n", - "\n", - "JampaRobo - 0 - 0\n", - "\n", - "BenicioJose0577 - 100000 - 1000\n", - "\n", - "eloirwschutz - 4 - 22\n", - "\n", - "Dioguinho141 - 16 - 1796\n", - "\n", - "CRISTIA33075520 - 1 - 25\n", - "\n", - "AlziraAlmeida11 - 4 - 23\n", - "\n", - "lcrive - 100000 - 1000\n", - "\n", - "Carloso74139217 - 100000 - 1000\n", - "\n", - "DouglasCorraRi1 - 5 - 33\n", - "\n", - "sanzio_eduardo - 100000 - 1000\n", - "\n", - "hamarissi1 - 4 - 60\n", - "\n", - "Medeirosjz - 100000 - 1000\n", - "\n", - "Antonio12671876 - 100000 - 1000\n", - "\n", - "ArtInovar - 3 - 11\n", - "\n", - "IvoSantanaMarc1 - 4 - 15\n", - "\n", - "Brasil68195790 - 100000 - 1000\n", - "\n", - "Dri65B - 100000 - 1000\n", - "\n", - "SuperBolsomini1 - 100000 - 1000\n", - "\n", - "mfpecanha1 - 100000 - 1000\n", - "\n", - "arqueira_a - 100000 - 1000\n", - "\n", - "CludiaTanaka2 - 100000 - 1000\n", - "\n", - "Helena_Cabello1 - 2 - 11\n", - "\n", - "VeigaJuscelina - 100000 - 1000\n", - "\n", - "owoguinho - 100000 - 1000\n", - "\n", - "marilia_goretti - 0 - 21\n", - "\n", - "LuizAugustoPai4 - 6 - 38\n", - "\n", - "chocopoemlate16 - 1 - 16\n", - "\n", - "Joonbabykoya - 100000 - 1000\n", - "\n", - "zoldyevvil - 100000 - 1000\n", - "\n", - "predadoalfa - 6 - 219\n", - "\n", - "FePatriota1 - 3 - 19\n", - "\n", - "NandaAndretto - 100000 - 1000\n", - "\n", - "safetyjm - 100000 - 1000\n", - "\n", - "CarlosG82785363 - 1 - 60\n", - "\n", - "KP62A - 5 - 92\n", - "\n", - "marstwolf - 0 - 8123.0\n", - "\n", - "Marcos_11_66 - 0 - 37\n", - "\n", - "Rosiveti1 - 3 - 10\n", - "\n", - "uzusaske - 100000 - 1000\n", - "\n", - "vhsmessy - 100000 - 1000\n", - "\n", - "JMBBrasil - 100000 - 1000\n", - "\n", - "baia_canuto - 3 - 32\n", - "\n", - "pjmackerman - 4 - 16340\n", - "\n", - "EN30A - 100000 - 1000\n", - "\n", - "clara_kess - 3 - 94\n", - "\n", - "CesarNi85939384 - 3 - 10\n", - "\n", - "CHRBRYSHOR - 100000 - 1000\n", - "\n", - "PauloRo49195361 - 0 - 15\n", - "\n", - "AndrePenteado4 - 100000 - 1000\n", - "\n", - "Marina92011959 - 2 - 39\n", - "\n", - "Marcos_28_11_66 - 0 - 9\n", - "\n", - "bnqzyy_jkv - 100000 - 1000\n", - "\n", - "FATIMAC75843178 - 2 - 9\n", - "\n" - ] - } - ], - "source": [ - "#Incluir uma dedida da distancia temporal entre twittes (mediana e mínimo)\n", - "df_handles['Tempo mediano'] = np.array(len(df_handles))\n", - "df_handles['Tempo menor'] = np.array(len(df_handles))\n", - "iuser = 0\n", - "for user in df_handles['handle']:\n", - " df_temp = df_timeline[df_timeline['tweet_author'] == user]\n", - " itweet = 0\n", - " menor = 100000\n", - " difs = list()\n", - " tweet_date_prev = None\n", - " for tweet in df_temp['tweet_created_at']:\n", - " tweet_date = pd.to_datetime(pd.to_datetime(tweet).strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\"))\n", - " if itweet > 0:\n", - " dif = (tweet_date_prev - tweet_date).seconds\n", - " if dif < menor:\n", - " menor = dif\n", - " difs.append(dif)\n", - " else:\n", - " tweet_date_prev = tweet_date\n", - " tweet_date_prev = tweet_date\n", - " itweet += 1\n", - " if len(difs) > 0:\n", - " mediana = statistics.median(difs)\n", - " else:\n", - " mediana = 1000\n", - " print(user + ' - ' + str(menor) + ' - ' + str(mediana)+'\\n')\n", - " df_handles['Tempo mediano'][iuser] = mediana\n", - " df_handles['Tempo menor'][iuser] = menor\n", - " iuser += 1\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BG-iNlU3vEnq" - }, - "source": [ - "**Os dados inicialmente tratados são reunidos com a classificação dada pelo INCT-DD**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 348 - }, - "id": "ppTFMTsTvEnq", - "outputId": "1b38577d-409d-45fa-b30b-0172311fcc6e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1072\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...followers_countfriends_countlanglocationnameprofile_imagetwitter_idtwitter_is_protectedverifiedwithheld_in_countries
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...21.0108.00.0Brasil, São PauloLeandro Matheshttp://pbs.twimg.com/profile_images/1141547105...52253248.00.00.0[]
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tweet_authortweet_text
0100_bolsonaro@OracoesB @wander_fabricio @DinhaCarvalho8 #Bo...
113valber1RT @leandroruschel: Tente encontrar na extrema...
21976MncRT @MinEconomia: “Nós estamos assistindo a uma...
3ACamargo241RT @juliovschneider: Se liga na viatura daqui ...
4AControldCarro Pajero TR4 4X4 Automatica, podendo sair ...
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tweet_authortweet_hashtags
0100_bolsonaro['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...
113valber1[], [], [], [], [], [], [], [], [], [], [], []...
21976Mnc[], [], [], [], [], [], ['PLP235NÃO'], [], ['P...
3ACamargo241[], [], [], [], [], [], [], [], [], [], [], []...
4AControld['RedeBBB', 'tbt', 'iphone', 'apple'], ['Natal...
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tweet_authortweet_source
0100_bolsonaroTwitter Web App, Twitter Web App, Twitter Web ...
113valber1Twitter for Android, Twitter for Android, Twit...
21976MncTwitter for iPhone, Twitter for iPhone, Twitte...
3ACamargo241Twitter for Android, Twitter for Android, Twit...
4AControldTwitter Web App, Twitter Web App, Twitter Web ...
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tweet_authorretweet_tratado
0100_bolsonaronão, não, não, não, não, não, não, não, não, n...
113valber1não, não, não, não, não, não, não, não, não, n...
21976Mncnão, não, não, não, não, não, não, não, não, n...
3ACamargo241não, não, não, não, não, sim, não, não, não, n...
4AControldnão, não, não, não, não, não, não, não, não, n...
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tweet_authortweet_com_rt_tratado
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tweet_authorretweet_e_tweet_com_rt_tratado
0100_bolsonaronão, não, sim, não, não, sim, sim, sim, não, n...
113valber1sim, sim, sim, sim, não, não, não, não, não, n...
21976Mncsim, sim, não, não, sim, sim, não, sim, sim, s...
3ACamargo241sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
4AControldnão, não, não, não, não, não, não, não, não, n...
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\n", - " " - ], - "text/plain": [ - " tweet_author retweet_e_tweet_com_rt_tratado\n", - "0 100_bolsonaro não, não, sim, não, não, sim, sim, sim, não, n...\n", - "1 13valber1 sim, sim, sim, sim, não, não, não, não, não, n...\n", - "2 1976Mnc sim, sim, não, não, sim, sim, não, sim, sim, s...\n", - "3 ACamargo241 sim, sim, sim, sim, sim, sim, sim, sim, sim, s...\n", - "4 AControld não, não, não, não, não, não, não, não, não, n..." - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Reune as informações da junção de retweets e tweets com rt\n", - "df_result_retweet_e_tweet_com_rt = df_timeline.groupby('tweet_author').agg({'retweet_e_tweet_com_rt_tratado':lambda col: ', '.join(col)}).reset_index()\n", - "df_result_retweet_e_tweet_com_rt.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZwA3QA7dvEns", - "outputId": "940c2e50-12c5-4790-a949-ebbb613b9230" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", - " \n", - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: FutureWarning: Passing 'suffixes' which cause duplicate columns {'tweet_author_x'} in the result is deprecated and will raise a MergeError in a future version.\n", - " \n" - ] - } - ], - "source": [ - "#Reune os dados (merge) do usuários, suas avaliações com texto dos tweets, as hashtags, as fontes e os retweets\n", - "df_result_merge = pd.merge(df_handles, df_users, on=['handle'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_hashtags, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_source, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_retweet, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_retweet_e_tweet_com_rt, left_on=['handle'], right_on=['tweet_author'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 365 - }, - "id": "DdtIwKDhvEnt", - "outputId": "b0871b69-9afa-4062-af03-325f72a059da" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "834\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_ytweet_hashtagstweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratado
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
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\n", - " " - ], - "text/plain": [ - " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", - "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", - "\n", - " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", - "0 não se aplica NaN \n", - "\n", - " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", - "0 não não \n", - "\n", - " Parece só Retweetar? ... tweet_author_y \\\n", - "0 não ... lemathes \n", - "\n", - " tweet_hashtags tweet_author_x \\\n", - "0 [], [], [], [], [], [], [], [], [], [], [], []... lemathes \n", - "\n", - " tweet_source tweet_author_y \\\n", - "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", - "\n", - " retweet_tratado tweet_author_x \\\n", - "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", - "\n", - " tweet_com_rt_tratado tweet_author_y \\\n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", - "\n", - " retweet_e_tweet_com_rt_tratado \n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... \n", - "\n", - "[1 rows x 46 columns]" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Exibe parte dos resultados da junção (nem todos os usuários ainda estão ativos e número de amostras diminui)\n", - "print(len(df_result_merge))\n", - "df_result_merge.head(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "T4Eyp5jEvEnt" - }, - "source": [ - "**A classificação dos usuários foi padronizada para 0 - Não Bot e 1 - Bot**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-6hG03d0vEnt", - "outputId": "2e88723c-8756-4da9-d437-f489c5e6eee6" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 0\n", - "1 0\n", - "2 0\n", - "3 1\n", - "4 0\n", - "Name: É Bot?, dtype: int64" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", - "df = df_result_merge\n", - "y = df['É Bot?'].apply(lambda x: 1 if (x == 'Sim' or x == 'sim') else 0)\n", - "y.reset_index(drop=True, inplace=True)\n", - "y.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Tsho3SNYvEnt" - }, - "outputs": [], - "source": [ - "##Seleciona as colunas para o conjunto X\n", - "#feature_cols = ['tweet_text'] #,'tweet_source','tweet_hashtags'\n", - "#x = df['tweet_text']\n", - "#x.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9Ds8AtqBvEnt" - }, - "source": [ - "** [Classficando apenas pelo texto dos Twittes (NLTK)] **" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nVJ-KWXJvEnt" - }, - "outputs": [], - "source": [ - "##Prepara o conjunto de dados para treinamento e teste\n", - "#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ifa_JwZuvEnu" - }, - "outputs": [], - "source": [ - "##Método para vetorizar e contabilizar os termos\n", - "stemmer = nltk.stem.RSLPStemmer()\n", - "class StemmedCountVectorizerRSLPS(CountVectorizer):\n", - " def build_analyzer(self):\n", - " analyzer = super(StemmedCountVectorizerRSLPS, self).build_analyzer()\n", - " return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n", - "stemmed_count_vect = StemmedCountVectorizerRSLPS(stop_words=nltk.corpus.stopwords.words('portuguese'))\n", - "tfidf_transformer = TfidfTransformer()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "1BTPUUcsvEnu" - }, - "outputs": [], - "source": [ - "##Pipeline para extrair as informaçoes e classificar com base no texto (pode ser usado ANN ou MNB [MultinomialNB(fit_prior=False)])\n", - "#text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),\n", - "# ('tfidf', TfidfTransformer()),\n", - "# ('mnb', MLPClassifier(random_state=1, max_iter=600, activation='relu',solver='adam')),\n", - "#])\n", - "#text_mnb_stemmed = text_mnb_stemmed.fit(x_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xyrFTNw-vEnu" - }, - "outputs": [], - "source": [ - "#text_mnb_stemmed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "teRgHViCvEnu" - }, - "outputs": [], - "source": [ - "##Avalia a classificação\n", - "#predicted_mnb_stemmed = text_mnb_stemmed.predict(x_test)\n", - "#np.mean(predicted_mnb_stemmed == y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PI4Z0JWlvEnu" - }, - "source": [ - "**Os atributos do treinamentos envolvem diversos fatores**\n", - "\n", - "Uma das etapas mais critícas da modelagem é a definição dos atributos que representam o cenário real, nesse sentido foram incluídas o máximo de variáveis que pudessem representar um usuário e suas atividades na rede, desde o tamanho do login escolhido até o tempo mínimo entre suas postagens. Na sequência são realizadas as atividades de extração, tratamento e junção dessas informações como atributos do conjunto de treinamento do modelo." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BqT8a9b1vEnv", - "outputId": "3c89be92-85a1-4d8d-a351-ab4479a418e4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['Unnamed: 0_x', 'Unnamed: 0.1', 'tabelaAmostra', 'p', 'É Bot?',\n", - " 'Se você fosse atribuir uma função ao bot, qual seria?', 'Função #2',\n", - " 'Comportamento agressivo?',\n", - " 'Comportamento repetitivo com # ou menções?', 'Parece só Retweetar?',\n", - " 'Só compartilha links?', 'Só faz comentários?',\n", - " 'Enaltece muito outros usuários?', 'Faz muito uso de emojis?',\n", - " 'Tem muitos posts sem textos?', 'Unnamed: 14', 'handle',\n", - " 'Tempo mediano', 'Tempo menor', 'Unnamed: 0_y', 'error', 'created_at',\n", - " 'default_profile', 'description', 'followers_count', 'friends_count',\n", - " 'lang', 'location', 'name', 'profile_image', 'twitter_id',\n", - " 'twitter_is_protected', 'verified', 'withheld_in_countries',\n", - " 'tweet_author_x', 'tweet_text', 'tweet_author_y', 'tweet_hashtags',\n", - " 'tweet_author_x', 'tweet_source', 'tweet_author_y', 'retweet_tratado',\n", - " 'tweet_author_x', 'tweet_com_rt_tratado', 'tweet_author_y',\n", - " 'retweet_e_tweet_com_rt_tratado'],\n", - " dtype='object')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns #df é o conjunto completo de dados, já com os twittes-hashtags-sources-retweets em campos únicos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 348 - }, - "id": "NB5JSYG7vEnv", - "outputId": "b060a608-bef3-47ac-f0d6-40501815efe2" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_ytweet_hashtagstweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratado
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
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\n", - " " - ], - "text/plain": [ - " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", - "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", - "\n", - " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", - "0 não se aplica NaN \n", - "\n", - " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", - "0 não não \n", - "\n", - " Parece só Retweetar? ... tweet_author_y \\\n", - "0 não ... lemathes \n", - "\n", - " tweet_hashtags tweet_author_x \\\n", - "0 [], [], [], [], [], [], [], [], [], [], [], []... lemathes \n", - "\n", - " tweet_source tweet_author_y \\\n", - "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", - "\n", - " retweet_tratado tweet_author_x \\\n", - "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", - "\n", - " tweet_com_rt_tratado tweet_author_y \\\n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", - "\n", - " retweet_e_tweet_com_rt_tratado \n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... \n", - "\n", - "[1 rows x 46 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MRrtKRFmvEnv" - }, - "source": [ - "De todo os conjuntos de informações disponíveis não foram selecionados aquelas que não poderiam ser automaticamente extraídos dos perfis e atividades dos usuários na rede. Portanto, as classificações como \"comportamento agressivo?\", \"Parece só Retweetar?\", entre outras, não foram incluídos no conjunto de treinamento." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "118Gy9p0vEnv" - }, - "outputs": [], - "source": [ - "feature_cols = ['followers_count', 'friends_count', 'Tempo mediano', 'Tempo menor']\n", - "x = df[feature_cols]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "II_ZwEPuvEnv" - }, - "outputs": [], - "source": [ - "##Converte os testos em frequências\n", - "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", - "#tfidf_transformer = TfidfTransformer()\n", - "#x_tfidf = tfidf_transformer.fit_transform(st)\n", - "#x_tfidf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6ndr7VXPvEnv" - }, - "outputs": [], - "source": [ - "##Inclui as frequências no conjunto x\n", - "#x_tfidf.shape\n", - "#x.join(pd.DataFrame(x_tfidf.todense()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XpdU-8UgvEnv", - "outputId": "49eb011c-1f5f-4f4a-80dc-6d6049de575a" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "100" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(df['tweet_hashtags'][7].replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\"))\n", - "len(df['tweet_hashtags'][7].split(\", [\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 414 - }, - "id": "Xv0OLiaRvEnw", - "outputId": "51b325ee-4113-4ac5-9acb-b9a5793b0140" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n", - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags media
021.0108.0191716130.130000
14192.04886.022120.020000
21341.01854.034260.060000
32.031.040791141200.425532
410.021.05849100.100000
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\n", - " " - ], - "text/plain": [ - " followers_count friends_count Tempo mediano Tempo menor \\\n", - "0 21.0 108.0 1917 16 \n", - "1 4192.0 4886.0 22 1 \n", - "2 1341.0 1854.0 34 2 \n", - "3 2.0 31.0 40791 141 \n", - "4 10.0 21.0 584 9 \n", - "\n", - " Quantidade hashtags Quantidade hashtags media \n", - "0 13 0.130000 \n", - "1 2 0.020000 \n", - "2 6 0.060000 \n", - "3 20 0.425532 \n", - "4 10 0.100000 " - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Inclui os quantitativos de hashtages utilizadas (e a mediana por postagem)\n", - "\n", - "qtd_hashtags = df['tweet_hashtags'].apply(lambda x: len(x.replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\")))\n", - "x['Quantidade hashtags'] = np.array(list(qtd_hashtags))\n", - "qtd_hashtags_media = df['tweet_hashtags'].apply(lambda x: len(x.replace(\"[\",\"\").replace(\"]\",\"\").replace(\", \\'\",\"$\").split(\"$\"))/len(x.split(\", [\")))\n", - "x['Quantidade hashtags media'] = np.array(list(qtd_hashtags_media))\n", - "\n", - "x.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TOlYNS-1vEnw", - "outputId": "fcef1884-34b4-49a2-c1af-7e025facab8e" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - } - ], - "source": [ - "#Inclui o número de dígitos no nome\n", - "username_digitos = df['handle'].apply(lambda x: sum(c.isdigit() for c in str(x)) ) \n", - "x['Digitos no username'] = np.array(list(username_digitos))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0IPxxrKxvEnw", - "outputId": "474df08a-1292-4929-b2e1-b9d453d8fddc" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " after removing the cwd from sys.path.\n", - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"\n" - ] - } - ], - "source": [ - "#O tamanho do nome e do login\n", - "tam_username = df['handle'].apply(lambda x: len(str(x)))\n", - "tam_nome = df['name'].apply(lambda x: len(str(x)))\n", - "x['Tamanho do username'] = np.array(list(tam_username))\n", - "x['Tamanho do nome'] = np.array(list(tam_nome))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 267 - }, - "id": "raLK-qY_vEnw", - "outputId": "665517cc-a532-480e-e665-b21a97fc9934" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nome
021.0108.0191716130.1300000814
14192.04886.022120.02000081513
21341.01854.034260.060000087
32.031.040791141200.4255320126
410.021.05849100.10000081534
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\n", - " " - ], - "text/plain": [ - " followers_count friends_count Tempo mediano Tempo menor \\\n", - "0 21.0 108.0 1917 16 \n", - "1 4192.0 4886.0 22 1 \n", - "2 1341.0 1854.0 34 2 \n", - "3 2.0 31.0 40791 141 \n", - "4 10.0 21.0 584 9 \n", - "\n", - " Quantidade hashtags Quantidade hashtags media Digitos no username \\\n", - "0 13 0.130000 0 \n", - "1 2 0.020000 8 \n", - "2 6 0.060000 0 \n", - "3 20 0.425532 0 \n", - "4 10 0.100000 8 \n", - "\n", - " Tamanho do username Tamanho do nome \n", - "0 8 14 \n", - "1 15 13 \n", - "2 8 7 \n", - "3 12 6 \n", - "4 15 34 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tjRssYzLvEnw" - }, - "source": [ - "A fonte do tweet foi considera importante informação, considerando que automações de postagens possam ser facilitadas a partir da versão Web ou que possa existir algum padrão no uso das diferentes fontes. Sendo assim, forneceu-se ao métodos a informação percentual da origem das postagens do mesmo usuário, seja Android, iPhone ou Web." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Y3HaOmS4vEnw" - }, - "outputs": [], - "source": [ - "#Calcula a quantidade de twittes por fontes\n", - "fonte_android = df['tweet_source'].apply(lambda x: str(x).count('Twitter for Android') )\n", - "fonte_iphone = df['tweet_source'].apply(lambda x: str(x).count('Twitter for iPhone') )\n", - "fonte_web = df['tweet_source'].apply(lambda x: str(x).count('Twitter Web App') )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eUSDhYGdvEnx" - }, - "outputs": [], - "source": [ - "fonte_soma = fonte_android + fonte_iphone + fonte_web\n", - "fonte_soma = fonte_soma.apply(lambda x: 1 if x <= 0 else x )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LQdbPscTvEnx" - }, - "outputs": [], - "source": [ - "#Calcula o percentual por usuário\n", - "fonte_android = fonte_android/fonte_soma\n", - "fonte_iphone = fonte_iphone/fonte_soma\n", - "fonte_web = fonte_web/fonte_soma" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 580 - }, - "id": "hfkQprbTvEnx", - "outputId": "ca9a2a12-1908-4431-cd52-a6afa3ee261b" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n", - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n", - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Web
021.0108.0191716130.13000008141.000.000.00
14192.04886.022120.020000815130.240.000.76
21341.01854.034260.0600000870.180.820.00
32.031.040791141200.42553201261.000.000.00
410.021.05849100.100000815340.001.000.00
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"x = x.fillna(0)\n", - "x.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SYxSo6k5vEnx", - "outputId": "b7658620-fa83-48d6-811c-0eae01d46f05" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 834.000000\n", - "mean 0.641682\n", - "std 0.463189\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 1.000000\n", - "75% 1.000000\n", - "max 1.000000\n", - "Name: Fonte de Android, dtype: float64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Avaliação geral das diferentes fontes\n", - "x['Fonte de Android'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PTtW4jOvvEnx", - "outputId": "4d1d8d39-f65e-449b-be9c-36ae23cc676a" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 834.000000\n", - "mean 0.198877\n", - "std 0.393385\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 1.000000\n", - "Name: Fonte de iPhone, dtype: float64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x['Fonte de iPhone'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iIFeXnIQvEnx", - "outputId": "391d3b15-288b-46d5-c2f9-858bc6b5dd12" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 834.000000\n", - "mean 0.149848\n", - "std 0.330788\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 1.000000\n", - "Name: Fonte de Web, dtype: float64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x['Fonte de Web'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "jE-W1fivvEnx", - "outputId": "17339649-893c-4f66-c7d4-8de7cbe65488" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 não, não, não, não, não, não, não, não, não, n...\n", - "1 sim, sim, não, sim, sim, sim, sim, não, sim, s...\n", - "2 não, não, não, não, sim, não, não, não, não, n...\n", - "3 não, não, não, não, não, não, não, não, não, n...\n", - "4 não, não, não, não, não, não, não, não, não, n...\n", - "Name: retweet_tratado, dtype: object" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Inclui a informação do retweet\n", - "df['retweet_tratado'].head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7lcoFmwvvEny" - }, - "outputs": [], - "source": [ - "retweet_tratado = df['retweet_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['retweet_tratado_media'] = np.array(list(retweet_tratado))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "1uXOoePGvEny" - }, - "outputs": [], - "source": [ - "tweet_com_rt = df['tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['tweet_com_rt_tratado_media'] = np.array(list(tweet_com_rt))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6KiugEWIvEny" - }, - "outputs": [], - "source": [ - "retweet_e_tweet_com_rt = df['retweet_e_tweet_com_rt_tratado'].apply(lambda x: str(x).count('sim')/len(x.split(\",\")))\n", - "x['retweet_e_tweet_com_rt_tratado_media'] = np.array(list(retweet_e_tweet_com_rt))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OSyDe2swvEny" - }, - "outputs": [], - "source": [ - "x_novo = x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ZAQWrF-rvEny" - }, - "outputs": [], - "source": [ - "##Inclui os textos dos twittes (NLTK)\n", - "#st = stemmed_count_vect.fit_transform((df['tweet_text']))\n", - "#tfidf_transformer = TfidfTransformer()\n", - "#x_tfidf = tfidf_transformer.fit_transform(st)\n", - "#x_tfidf\n", - "#x_novo = x.join(pd.DataFrame(x_tfidf.todense()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oYytkQlWvEny", - "outputId": "b80e141c-e46a-4e1f-b96a-8faa355b6651" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(834, 15)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_novo.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 305 - }, - "id": "0Zs-qHPsvEnz", - "outputId": "5b42c4e5-c5ec-4609-b7a1-af27c9c16089" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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"4 0.940000 " - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_novo.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ds5zbJqWvEnz" - }, - "source": [ - "**Com o primeiro conjunto de atributos formado é possível separar o conjunto de dados em treinamento e teste para a elaboração do modelo**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dZbiiGhAvEnz" - }, - "outputs": [], - "source": [ - "#Cria um modelo de classificação para o conjunto completo\n", - "x_train, x_test, y_train, y_test = train_test_split(x_novo, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zKaaJDpxvEnz", - "outputId": "4696dbcd-17a6-49f8-eddd-375e730f3522" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7330677290836654" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "np.mean(y_pred == y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "a14V0FEnvEnz" - }, - "outputs": [], - "source": [ - "##Seleciona os atributos mais \"importantes\"\n", - "#x_new = SelectKBest(chi2, k=20).fit_transform(x_novo, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GLO1GeHovEn2" - }, - "outputs": [], - "source": [ - "#x_train, x_test, y_train, y_test = train_test_split(x_new, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cuuGpOcdvEn3", - "outputId": "3d215a49-3720-4e80-e79d-6046044f3260" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.7330677290836654 | Balanced accuracy: 0.6958582834331337\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[ 49, 35],\n", - " [ 32, 135]])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "O5PS2y9hvEn3", - "outputId": "8a24eb8f-3b2e-4350-a1a2-ba3bd6ac6c6a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.60 0.58 0.59 84\n", - " 1 0.79 0.81 0.80 167\n", - "\n", - " accuracy 0.73 251\n", - " macro avg 0.70 0.70 0.70 251\n", - "weighted avg 0.73 0.73 0.73 251\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0y_Y_7uQvEn3", - "outputId": "6208c5ba-8f01-464a-ecd6-c3a8df824d2c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.7250996015936255 | Balanced accuracy: 0.6691616766467066\n" - ] - } - ], - "source": [ - "#Classificação com RNA\n", - "classifier = MLPClassifier(max_iter=1200, random_state=1, activation='tanh', solver='adam') #activation: logistic, relu, tanh, identity | solver: lbfgs, sgd, adam\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ki86QVDAvEn3" - }, - "source": [ - "**Informações de trend topics**\n", - "\n", - "Outra informação que se mostrou de relevância ao longo do trabalho de modelagem foi a relação das postagens de bots com as menções e hashtags listadas nos mais atuais 'trend topics', ou seja, o aparente uso de termos altamente utilizados no momento para possivelmente alavancar a visibilidade da postagem.\n", - "\n", - "Para averiguar essa possibilidade, um sistema de monitoramento dos tópicos mais mencionados foi criado e cada postagem coletada do usuário foi confrontado com os 'trend topics' do período mais próximo. Esse confrontamento gerou um percentual de uso desses tópicos nas postagens dos usuários." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 548 - }, - "id": "jnPs1tG6vEn3", - "outputId": "8bf209ff-69eb-4b45-86a1-fdb25d7ec3f2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2680\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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trending_idtrend_date_timetrenduser1_idtweet1user2_idtweet2user3_idtweet3user4_idtweet4user5_idtweet5
012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-
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trending_idtrend_date_timetrenduser1_idtweet1user2_idtweet2user3_idtweet3user4_idtweet4user5_idtweet5Trend Date Time Convertido
012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-2021-12-03
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-2021-12-03
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-2021-12-03
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-2021-12-03
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Jin, membro do BTS, está completando... 0 \n", - "3 - 0 \n", - "4 TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R... 0 \n", - "\n", - " tweet2 user3_id \\\n", - "0 - 0 \n", - "1 Como estão se sentindo com a nova indicação ao... 28431722 \n", - "2 - 0 \n", - "3 - 0 \n", - "4 - 0 \n", - "\n", - " tweet3 user4_id \\\n", - "0 - 0 \n", - "1 Vocês se preocupam com o futuro agora que já r... 78148969 \n", - "2 - 0 \n", - "3 - 0 \n", - "4 - 0 \n", - "\n", - " tweet4 user5_id tweet5 \\\n", - "0 - 0 - \n", - "1 OH Léo Dias eu vou mandar a fatura pra você, d... 0 - \n", - "2 - 0 - \n", - "3 - 0 - \n", - "4 - 0 - \n", - "\n", - " Trend Date Time Convertido \n", - "0 2021-12-03 \n", - "1 2021-12-03 \n", - "2 2021-12-03 \n", - "3 2021-12-03 \n", - "4 2021-12-03 " - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Inclui um percentual de trending topics utilizado por tweet\n", - "#Para tweet, busca pelos trending topics imediatamente anteriores\n", - "df_timeline['Numero de trendings'] = np.array(len(df_timeline))\n", - "df_timeline['Numero de trendings'] = 0\n", - "df_trends['Trend Date Time Convertido'] = np.array(len(df_trends))\n", - "\n", - "itrend = 0\n", - "for x in df_trends['trend_date_time']:\n", - " df_trends['Trend Date Time Convertido'][itrend] = pd.to_datetime(x).strftime(\"%Y-%m-%d\")\n", - " itrend += 1\n", - "\n", - "df_trends.head() " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wjdvEEvjvEn4" - }, - "source": [ - "O relacionamento dos trends e dos tweets foi realizado percorrendo todos os trends armazenados para cada tweet em data anterior ao do tweet e, para cada trend nessa condição, verificou-se no texto do tweet a presença de trendings. Caso esteja presente acumulou-se essa ocorrência, finalizando com a ocorrência de uso de uma trend por cada tweet.\n", - "Este trecho demanda de melhorias em desempenho e na inclusão de restrições que reduzam o tempo de ocorrência da trend para mais próximo do tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iR3IPD8jvEn4", - "outputId": "e3d89551-126a-499f-9847-b91dd19ca5b3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "19\n", - "20\n", - "21\n", - "22\n", - "23\n", - "24\n", - "25\n", - "26\n", - "27\n", - "28\n", - "29\n", - "30\n", - "31\n", - "32\n", - "33\n", - "34\n", - "35\n", - "36\n", - "37\n", - "38\n", - "39\n", - "40\n", - "41\n", - "42\n", - "43\n", - "44\n", - 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" if df_timeline['tweet_text'][itweet].find(df_trends['trend'][itrend]) != -1: \n", - " df_timeline['Numero de trendings'][itweet] = df_timeline['Numero de trendings'][itweet] + 1\n", - " itrend += 1\n", - " print(itweet) \n", - " itweet += 1 " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IIb6GW_ZvEn4" - }, - "source": [ - "Para cada tweet foi armazenados o número de trend topics encontrado." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ulw5xkusvEn4", - "outputId": "8f1d9f5d-68fc-4733-bfa5-4384b1a45542" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "count 82413.000000\n", - "mean 0.001262\n", - "std 0.036843\n", - "min 0.000000\n", - "25% 0.000000\n", - "50% 0.000000\n", - "75% 0.000000\n", - "max 3.000000\n", - "Name: Numero de trendings, dtype: float64" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_timeline[df_timeline['Numero de trendings'] > 0].describe()\n", - "df_timeline['Numero de trendings'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XbYCwlmnvEn4", - "outputId": "c26440fb-41e6-4871-b9e0-103471bfae65" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(82413, 22)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_timeline.head(3)\n", - "df_timeline.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oBqtGHSevEn4" - }, - "source": [ - "As quantidades de trendings utilizadas em cada tweet foram agrupados por autor (usuário), assim foram incluídos na base de treinamento o número de trendings utilizadas, a média de trendings por tweet desse autor e o número máximo de trendings usado em um mesmo tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "VIZBn0qZvEn4", - "outputId": "c3b0bf5b-07c2-47d6-8339-f3942b7a122d" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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tweet_authorNumero de trendingstrends_max
0100_bolsonaro00
113valber100
21976Mnc00
3ACamargo24100
4AControld33
............
830wolfjorge20100
831yoshio_carlos00
832zemariasccp100
833zeplu100
834zfabrogmailcom00
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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handleTempo medianoTempo menor
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNlemathes191716
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNMaurcio98905595221
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334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonãoNaNfelipeleixas40791141
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonãonãonãonãonãonãoNaNJoseCar414511945849
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012021-12-03 21:03:31.034742#HappyBirthdayJin0-0-0-0-0-2021-12-03
122021-12-03 21:03:31.286371suga28431722Começou!\\n\\nEles estão todos de terno e sentad...28431722Como estão se sentindo com a nova indicação ao...28431722Vocês se preocupam com o futuro agora que já r...78148969OH Léo Dias eu vou mandar a fatura pra você, d...0-2021-12-03
232021-12-03 21:03:31.417346#JINDAY132699857REIZINHO! Jin, membro do BTS, está completando...0-0-0-0-2021-12-03
342021-12-03 21:03:31.527791#playplusmudo0-0-0-0-0-2021-12-03
452021-12-03 21:03:31.720859TE AMAMOS DAYANE MELLO34590687TE AMAMOS DAYANE MELLO ❤️🍷 https://t.co/RcyA8R...0-0-0-0-2021-12-03
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_ytweet_hashtagstweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratado
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathes[], [], [], [], [], [], [], [], [], [], [], []...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595[], [], [], [], [], [], [], [], [], [], [], []...Maurcio98905595Twitter for Android, Twitter Web App, Twitter ...Maurcio98905595sim, sim, não, sim, sim, sim, sim, não, sim, s...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunViana[], [], [], [], [], [], [], [], [], [], [], []...LunVianaTwitter for iPhone, Twitter for Android, Twitt...LunViananão, não, não, não, sim, não, não, não, não, n...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixas[], ['EuApoioVotoImpresso'], [], ['GloboLixo']...felipeleixasTwitter for Android, Twitter for Android, Twit...felipeleixasnão, não, não, não, não, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194[], [], [], [], [], [], [], [], [], [], ['OsPi...JoseCar41451194Twitter for iPhone, Twitter for iPhone, Twitte...JoseCar41451194não, não, não, não, não, não, não, não, não, n...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratadotweet_author_xNumero de trendings_xtrends_mediatweet_author_yNumero de trendings_ytrends_max
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes0.000.00lemathes00
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...Maurcio98905595não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio9890559500
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...LunVianasim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...LunViana0.010.01LunViana11
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas00
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar414511940.000.00JoseCar4145119400
..................................................................
82910661067https://twitter.com/@CesarNi859393841111.csvSimRetweetarNaNnãosimsim...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi85939384sim, sim, sim, sim, sim, sim, sim, sim, sim, s...CesarNi859393840.000.00CesarNi8593938400
83010681069https://twitter.com/@PauloRo491953611111.csvSimRetweetarNaNnãosimsim...PauloRo49195361não, sim, sim, sim, não, sim, sim, sim, sim, s...PauloRo49195361não, sim, sim, sim, não, sim, sim, sim, sim, s...PauloRo491953610.000.00PauloRo4919536100
83110701071https://twitter.com/@Marina920119591111.csvSimRetweetarNaNnãonãosim...Marina92011959não, não, não, não, não, sim, não, não, não, n...Marina92011959não, não, não, não, não, sim, não, não, não, n...Marina920119590.000.00Marina9201195900
83210711072https://twitter.com/@Marcos_28_11_661111.csvSimRetweetarNaNnãonãonão...Marcos_28_11_66sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Marcos_28_11_66sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Marcos_28_11_660.000.00Marcos_28_11_6600
83310731074https://twitter.com/@FATIMAC758431781111.csvSimRetweetarNaNnãosimsim...FATIMAC75843178não, sim, sim, não, não, não, não, não, não, n...FATIMAC75843178não, sim, sim, não, não, não, não, não, não, n...FATIMAC758431780.000.00FATIMAC7584317800
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Se você fosse atribuir uma função ao bot, qual seria? \\\n", - "0 0000.csv não não se aplica \n", - "1 0000.csv não não se aplica \n", - "2 0000.csv não não se aplica \n", - "3 0000.csv sim Publicar hashtags \n", - "4 0000.csv Não não se aplica \n", - ".. ... ... ... \n", - "829 1111.csv Sim Retweetar \n", - "830 1111.csv Sim Retweetar \n", - "831 1111.csv Sim Retweetar \n", - "832 1111.csv Sim Retweetar \n", - "833 1111.csv Sim Retweetar \n", - "\n", - " Função #2 Comportamento agressivo? \\\n", - "0 NaN não \n", - "1 NaN não \n", - "2 NaN não \n", - "3 Atacar sim \n", - "4 NaN não \n", - ".. ... ... \n", - "829 NaN não \n", - "830 NaN não \n", - "831 NaN não \n", - "832 NaN não \n", - "833 NaN não \n", - "\n", - " Comportamento repetitivo com # ou menções? Parece só Retweetar? ... \\\n", - "0 não não ... \n", - "1 não não ... \n", - "2 não não ... \n", - "3 sim não ... \n", - "4 não não ... \n", - ".. ... ... ... \n", - "829 sim sim ... \n", - "830 sim sim ... \n", - "831 não sim ... \n", - "832 não não ... \n", - "833 sim sim ... \n", - "\n", - " tweet_author_x tweet_com_rt_tratado \\\n", - "0 lemathes não, sim, não, não, não, sim, sim, sim, sim, s... \n", - "1 Maurcio98905595 não, não, sim, não, não, sim, não, sim, não, n... \n", - "2 LunViana sim, sim, sim, sim, não, sim, sim, sim, sim, s... \n", - "3 felipeleixas não, não, não, não, sim, não, não, não, não, n... \n", - "4 JoseCar41451194 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - ".. ... ... \n", - "829 CesarNi85939384 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "830 PauloRo49195361 não, sim, sim, sim, não, sim, sim, sim, sim, s... \n", - "831 Marina92011959 não, não, não, não, não, sim, não, não, não, n... \n", - "832 Marcos_28_11_66 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "833 FATIMAC75843178 não, sim, sim, não, não, não, não, não, não, n... \n", - "\n", - " tweet_author_y retweet_e_tweet_com_rt_tratado \\\n", - "0 lemathes não, sim, não, não, não, sim, sim, sim, sim, s... \n", - "1 Maurcio98905595 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "2 LunViana sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "3 felipeleixas não, não, não, não, sim, não, não, não, não, n... \n", - "4 JoseCar41451194 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - ".. ... ... \n", - "829 CesarNi85939384 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "830 PauloRo49195361 não, sim, sim, sim, não, sim, sim, sim, sim, s... \n", - "831 Marina92011959 não, não, não, não, não, sim, não, não, não, n... \n", - "832 Marcos_28_11_66 sim, sim, sim, sim, sim, sim, sim, sim, sim, s... \n", - "833 FATIMAC75843178 não, sim, sim, não, não, não, não, não, não, n... \n", - "\n", - " tweet_author_x Numero de trendings_x trends_media tweet_author_y \\\n", - "0 lemathes 0.00 0.00 lemathes \n", - "1 Maurcio98905595 0.00 0.00 Maurcio98905595 \n", - "2 LunViana 0.01 0.01 LunViana \n", - "3 felipeleixas 0.00 0.00 felipeleixas \n", - "4 JoseCar41451194 0.00 0.00 JoseCar41451194 \n", - ".. ... ... ... ... \n", - "829 CesarNi85939384 0.00 0.00 CesarNi85939384 \n", - "830 PauloRo49195361 0.00 0.00 PauloRo49195361 \n", - "831 Marina92011959 0.00 0.00 Marina92011959 \n", - "832 Marcos_28_11_66 0.00 0.00 Marcos_28_11_66 \n", - "833 FATIMAC75843178 0.00 0.00 FATIMAC75843178 \n", - "\n", - " Numero de trendings_y trends_max \n", - "0 0 0 \n", - "1 0 0 \n", - "2 1 1 \n", - "3 0 0 \n", - "4 0 0 \n", - ".. ... ... \n", - "829 0 0 \n", - "830 0 0 \n", - "831 0 0 \n", - "832 0 0 \n", - "833 0 0 \n", - "\n", - "[834 rows x 52 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_result_merge = pd.merge(df_result_merge,df_result_trend, left_on=['handle'], right_on=['tweet_author'])\n", - "df_result_merge = pd.merge(df_result_merge,df_result_trend_max, left_on=['handle'], right_on=['tweet_author'])\n", - 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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratadotweet_author_xNumero de trendings_xtrends_mediatweet_author_yNumero de trendings_ytrends_maxqtdtrends
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112https://twitter.com/@Maurcio989055950000.csvnãonão se aplicaNaNnãonãonão...não, não, sim, não, não, sim, não, sim, não, n...Maurcio98905595sim, sim, sim, sim, sim, sim, sim, sim, sim, s...Maurcio989055950.000.00Maurcio989055950015
223https://twitter.com/@LunViana0000.csvnãonão se aplicaNaNnãonãonão...sim, sim, sim, sim, não, sim, sim, sim, sim, s...LunVianasim, sim, sim, sim, sim, sim, sim, sim, sim, s...LunViana0.010.01LunViana118
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnão...não, não, não, não, sim, não, não, não, não, n...felipeleixasnão, não, não, não, sim, não, não, não, não, n...felipeleixas0.000.00felipeleixas0012
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplicaNaNnãonãonão...sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar41451194sim, sim, sim, sim, sim, sim, sim, sim, sim, s...JoseCar414511940.000.00JoseCar414511940015
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followers_countfriends_countTempo medianoTempo menorQuantidade hashtagsQuantidade hashtags mediaDigitos no usernameTamanho do usernameTamanho do nomeFonte de AndroidFonte de iPhoneFonte de Webretweet_tratado_mediatweet_com_rt_tratado_mediaretweet_e_tweet_com_rt_tratado_mediaqtdtrendstrends_mediatrends_max
021.0108.0191716130.13000008141.000.000.000.100.7500000.84000080.000
14192.04886.022120.020000815130.240.000.760.540.5200000.970000150.000
21341.01854.034260.0600000870.180.820.000.080.8400000.91000080.011
32.031.040791141200.42553201261.000.000.000.000.0425530.042553120.000
410.021.05849100.100000815340.001.000.000.000.9400000.940000150.000
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\n", - " " - ], - "text/plain": [ - " followers_count friends_count Tempo mediano Tempo menor \\\n", - "0 21.0 108.0 1917 16 \n", - "1 4192.0 4886.0 22 1 \n", - "2 1341.0 1854.0 34 2 \n", - "3 2.0 31.0 40791 141 \n", - "4 10.0 21.0 584 9 \n", - "\n", - " Quantidade hashtags Quantidade hashtags media Digitos no username \\\n", - "0 13 0.130000 0 \n", - "1 2 0.020000 8 \n", - "2 6 0.060000 0 \n", - "3 20 0.425532 0 \n", - "4 10 0.100000 8 \n", - "\n", - " Tamanho do username Tamanho do nome Fonte de Android Fonte de iPhone \\\n", - "0 8 14 1.00 0.00 \n", - "1 15 13 0.24 0.00 \n", - "2 8 7 0.18 0.82 \n", - "3 12 6 1.00 0.00 \n", - "4 15 34 0.00 1.00 \n", - "\n", - " Fonte de Web retweet_tratado_media tweet_com_rt_tratado_media \\\n", - "0 0.00 0.10 0.750000 \n", - "1 0.76 0.54 0.520000 \n", - "2 0.00 0.08 0.840000 \n", - "3 0.00 0.00 0.042553 \n", - "4 0.00 0.00 0.940000 \n", - "\n", - " retweet_e_tweet_com_rt_tratado_media qtdtrends trends_media trends_max \n", - "0 0.840000 8 0.00 0 \n", - "1 0.970000 15 0.00 0 \n", - "2 0.910000 8 0.01 1 \n", - "3 0.042553 12 0.00 0 \n", - "4 0.940000 15 0.00 0 " - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_novo_trend.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jrJgWnrJvEn6" - }, - "source": [ - "**Conjuntos de treinamento e teste**\n", - "\n", - "Os dados reunidos para geração dos modelos são, então, separados em dados de treinamento e teste para a aplicação dos métodos de aprendizagem de máquina - em especial Random Florest, Redes neuronais artificiais e Gradient Boosting." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "r2Ydk5gJvEn6" - }, - "outputs": [], - "source": [ - "x_train, x_test, y_train, y_test = train_test_split(x_novo_trend, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xmo-1VWTvEn6", - "outputId": "e09bfe11-58e3-40ff-e69d-575c5a045a55" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.7410358565737052 | Balanced accuracy: 0.6929712004562304\n", - "Score: 0.7410358565737052\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[ 46, 38],\n", - " [ 27, 140]])" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ySGFI_0UvEn6", - "outputId": "5c0d18c3-b7d6-438b-f24a-f7b00d5b8654" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 0.749003984063745 | Balanced accuracy: 0.6900841174793271\n", - "Score: 0.749003984063745\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[ 43, 41],\n", - " [ 22, 145]])" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classifier = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=1)\n", - "classifier = classifier.fit(x_train,y_train)\n", - "y_pred = classifier.predict(x_test)\n", - "mean = np.mean(y_pred == y_test)\n", - "balanced = balanced_accuracy_score(y_test, y_pred)\n", - "print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "print(\"Score: \" + str(classifier.score(x_test, y_test)))\n", - "confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 374 - }, - "id": "bg7cFJ8VvEn6", - "outputId": "fff005c6-51d0-4864-cfb5-732ef207f224" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "importances = classifier.feature_importances_\n", - "\n", - "indices = np.argsort(importances)\n", - "\n", - "fig, ax = plt.subplots(figsize =(10, 6))\n", - "ax.barh(range(len(importances)), importances[indices])\n", - "ax.set_yticks(range(len(importances)))\n", - "_ = ax.set_yticklabels(np.array(x_novo_trend.columns)[indices])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kN336CjPvEn6" - }, - "source": [ - "**Resultados**\n", - "\n", - "Os resultados ainda demandam de maior avaliação, especialmente com a variação da semente aleatória para os cortes do conjunto de treinamento e para a aplicação dos métodos. Ainda nesse sentido, demanda-se ainda da seleção de modelos baseada na otimização dos hiperparâmetros dos métodos aplicados.\n", - "\n", - "Mesmo com essas demandas, observa-se uma acurácia aproximada de 74% para os métodos (e aproximadamente 70% ao considerar-se o desbalanceamento da base). Valor considerado bom, dado o complexo cenário tratado. \n", - "\n", - "Importante ponto a ser destacado que o valor da acurácia baseia-se também em um ponto de corte da consistência da classificação, a qual pode variar en 0.0 e 1.0, valores que atrelam-se à probabilidade da classificação, em que por padrão adota-se o corte em 0.5, apesar da aplicação pode gerar um intervalo mais restrito, deslocando a média/mediana das predições. Dito isso e considerando que não deva ser utilizado apenas o corte \"bruto\" de bot ou não bot, a associação dessa probabilidade permite melhor compreensão do \"risco\" do usuário ser efetivamente um bot, bem como permite um deslocamento do rigor dessa classificação. \n", - "\n", - "Os trechos a seguir avaliam a acurácia considerando a mediana das predições como corte, bem como a comparação dos valores preditos nos grupos de usuários previamente (manualmente) classificados como bot ou não, no qual verifica-se uma clara separação dos valores preditos." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MFWM1W5pvEn6" - }, - "outputs": [], - "source": [ - "#x_new_trend = SelectKBest(chi2, k=10).fit_transform(x_novo_trend, y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MMZ0DPRDvEn7" - }, - "outputs": [], - "source": [ - "#x_train, x_test, y_train, y_test = train_test_split(x_new_trend, y, test_size=0.3, random_state=1) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XPiyVsitvEn7" - }, - "outputs": [], - "source": [ - "#classifier = RandomForestClassifier(n_jobs=3, random_state=1, n_estimators=100)\n", - "#classifier = classifier.fit(x_train,y_train)\n", - "#y_pred = classifier.predict(x_test)\n", - "#mean = np.mean(y_pred == y_test)\n", - "#balanced = balanced_accuracy_score(y_test, y_pred)\n", - "#print (\"Mean: \" + str(mean) + \" | Balanced accuracy: \" + str(balanced))\n", - "#confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "f4DmJ2b6vEn7" - }, - "outputs": [], - "source": [ - "#x_new_trend" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ijck93gzvEn7" - }, - "outputs": [], - "source": [ - "#confusion_matrix(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - 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"execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 337 - }, - "id": "0KZc6CgBvEn8", - "outputId": "39b9f003-48ea-4b82-9da2-9ab86bf8b89a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "834\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(len(x_test_geral))\n", - "y_test_temp = y_test\n", - "y_test_temp.reset_index(drop=True, inplace=True)\n", - "y_test_temp[y_test_temp == 1].index\n", - "res_geral = classifier.predict_proba(x_test_geral)[y_test_temp.index,1]\n", - "res_sim = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 1].index,1]\n", - "res_nao = classifier.predict_proba(x_test_geral)[y_test_temp[y_test_temp == 0].index,1]\n", - "\n", - "np.median(res_sim)\n", - "np.median(res_nao)\n", - "bplots = plt.boxplot([res_geral, res_nao, res_sim], vert = 1, patch_artist = False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 300 - }, - "id": "GCvfdnSFvEn8", - "outputId": "6920e7b1-40da-444c-f684-a593b3e0bfc8" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Não
count84.000000
mean0.479382
std0.284891
min0.014898
25%0.232253
50%0.475195
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Sim
count167.000000
mean0.767758
std0.233861
min0.030042
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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handle
001https://twitter.com/@lemathes0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0lemathes
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0Maurcio98905595
223https://twitter.com/@LunViana0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0LunViana
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonão0felipeleixas
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplica0nãonãonãonãonãonãonãonão0JoseCar41451194
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\n", - "
\n", - " " - ], - "text/plain": [ - " Unnamed: 0 Unnamed: 0.1 tabelaAmostra p \\\n", - "0 0 1 https://twitter.com/@lemathes 0000.csv \n", - "1 1 2 https://twitter.com/@Maurcio98905595 0000.csv \n", - "2 2 3 https://twitter.com/@LunViana 0000.csv \n", - "3 3 4 https://twitter.com/@felipeleixas 0000.csv \n", - "4 4 5 https://twitter.com/@JoseCar41451194 0000.csv \n", - "\n", - " É Bot? Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", - "0 não não se aplica 0 \n", - "1 não não se aplica 0 \n", - "2 não não se aplica 0 \n", - "3 sim Publicar hashtags Atacar \n", - "4 Não não se aplica 0 \n", - "\n", - " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", - "0 não não \n", - "1 não não \n", - "2 não não \n", - "3 sim sim \n", - "4 não não \n", - "\n", - " Parece só Retweetar? Só compartilha links? Só faz comentários? \\\n", - "0 não não não \n", - "1 não não não \n", - "2 não não não \n", - "3 não não não \n", - "4 não não não \n", - "\n", - " Enaltece muito outros usuários? Faz muito uso de emojis? \\\n", - "0 não não \n", - "1 não não \n", - "2 não não \n", - "3 não não \n", - "4 não não \n", - "\n", - " Tem muitos posts sem textos? Unnamed: 14 handle \n", - "0 não 0 lemathes \n", - "1 não 0 Maurcio98905595 \n", - "2 não 0 LunViana \n", - "3 não 0 felipeleixas \n", - "4 não 0 JoseCar41451194 " - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Lê os dados da aplicação do botometer\n", - "#Busca os dados dos usuários avaliados\n", - "datafile_botometer = \"/content/sample_data/handles_inct.csv\"\n", - "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", - "#Preenche os valores NaN con 0 apenas para avaliação geral\n", - "df_botometer = df_botometer.fillna(0)\n", - "print(len(df_botometer))\n", - "df_botometer.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 502 - }, - "id": "dREze2TlvEn9", - "outputId": "038a129e-2839-4009-e09d-062f956dedd5" - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Avalia os resultados do botometer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'não'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Não'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sim'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Sim'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" + \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3460\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'" - ] - } - ], - "source": [ - "#Avalia os resultados do botometer\n", - "a = len(df_botometer['analise_botometer'])\n", - "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", - "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", - "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", - "botometer_geral = df_botometer['analise_botometer']\n", - "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", - "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 253 - }, - "id": "DzmZgqDkvEn9", - "outputId": "e8e8ddbf-28de-427e-a18d-314c97c0a804" - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#(11, 6)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbplots\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbotometer_geral\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbotometer_nao\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbotometer_sim\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_geral\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_nao\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_sim\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'blue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightblue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightgreen'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pink'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbplot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbplots\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'botometer_geral' is not defined" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize =(20, 10)) #(11, 6)\n", - "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", - "colors = ['blue', 'green', 'red', 'lightblue', 'lightgreen', 'pink']\n", - "c = 0\n", - "for i, bplot in enumerate(bplots['boxes']):\n", - " bplot.set(color=colors[c], linewidth=3)\n", - " c += 1\n", - " \n", - "colorss = ['blue','blue', 'green', 'green', 'red', 'red', 'lightblue', 'lightblue', 'lightgreen', 'lightgreen', 'pink', 'pink' ] \n", - "c3 = 0\n", - "for cap in bplots['caps']:\n", - " cap.set(color=colorss[c3], linewidth=3)\n", - " c3 +=1\n", - "\n", - "plt.title(\"Boxplot da avaliação do Botometer e do novo modelo Pegabot para os dados avaiados no INCT-DD\", loc=\"center\", fontsize=18)\n", - "plt.xlabel(\"Agrupados por: (1) Botometer Geral; (2) Botometer apenas considerados não bots; (3) Botometer apenas considerados bots; (4) Novo Pegabot Geral; (5) Novo Pegabot apenas considerados não bots; (6) Novo Pegabot apenas considerados bots\")\n", - "plt.ylabel(\"Avaliação do Botometer\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Mc5WWVrevEn9" - }, - "outputs": [], - "source": [ - "import scipy\n", - "scipy.stats.kruskal(botometer_geral, botometer_nao,botometer_sim)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "H-9ZYAcPvEn9" - }, - "outputs": [], - "source": [ - "scipy.stats.kruskal(res_geral, res_nao,res_sim)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_XSWWa_1lwQm" - }, - "source": [ - "

Análise de Sentimento

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Djeb7PUI77DC", - "outputId": "249f99d9-0620-4961-f224-ec93cf564d8b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n", - "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n", - "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n", - "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n", - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: wordcloud in /usr/local/lib/python3.7/dist-packages (1.8.2.2)\n", - "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from wordcloud) (1.21.6)\n", - "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from wordcloud) (7.1.2)\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from wordcloud) (3.2.2)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (1.4.4)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (0.11.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (2.8.2)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (3.0.9)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (4.1.1)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n" - ] - } - ], - "source": [ - "!pip install nltk\n", - "!pip install wordcloud" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "joxis7kss1II", - "outputId": "f4abf21f-d2a0-472b-e628-a3098cd93de0" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import string\n", - "import nltk\n", - "nltk.download('stopwords')\n", - "from nltk.corpus import stopwords\n", - "from nltk.probability import FreqDist\n", - "from wordcloud import WordCloud, STOPWORDS\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_error\n", - "from sklearn.feature_extraction.text import CountVectorizer\n", - "from sklearn.feature_extraction.text import TfidfTransformer\n", - "from sklearn.naive_bayes import MultinomialNB\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LHgK1OAttT08", - "outputId": "4c1edee4-d3e6-4a14-c843-e67601cacdc3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 não\n", - "1 não\n", - "2 não\n", - "3 sim\n", - "4 não\n", - "Name: Comportamento agressivo?, dtype: object" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_handles['Comportamento agressivo?'].head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 365 - }, - "id": "UixBJa39kLDg", - "outputId": "225d4057-63aa-4ee6-fd69-daf5f84d961b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "834\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Unnamed: 0_xUnnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?...tweet_author_xtweet_sourcetweet_author_yretweet_tratadotweet_author_xtweet_com_rt_tratadotweet_author_yretweet_e_tweet_com_rt_tratadotweet_authortweet_text_y
001https://twitter.com/@lemathes0000.csvnãonão se aplicaNaNnãonãonão...lemathesTwitter for Android, Twitter for Android, Twit...lemathesnão, não, não, não, não, não, não, não, não, n...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathesnão, sim, não, não, não, sim, sim, sim, sim, s...lemathes@LucianoHangBr Já demorou muito!, RT @LucianoH...
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\n", - " " - ], - "text/plain": [ - " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", - "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", - "\n", - " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", - "0 não se aplica NaN \n", - "\n", - " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", - "0 não não \n", - "\n", - " Parece só Retweetar? ... tweet_author_x \\\n", - "0 não ... lemathes \n", - "\n", - " tweet_source tweet_author_y \\\n", - "0 Twitter for Android, Twitter for Android, Twit... lemathes \n", - "\n", - " retweet_tratado tweet_author_x \\\n", - "0 não, não, não, não, não, não, não, não, não, n... lemathes \n", - "\n", - " tweet_com_rt_tratado tweet_author_y \\\n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", - "\n", - " retweet_e_tweet_com_rt_tratado tweet_author \\\n", - "0 não, sim, não, não, não, sim, sim, sim, sim, s... lemathes \n", - "\n", - " tweet_text_y \n", - "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... \n", - "\n", - "[1 rows x 48 columns]" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Seleção do texto e com o rótulo é agressivo ou não\n", - "df_result_merge_text = pd.merge(df_handles, df_users, on=['handle'])\n", - "df_result_merge_text = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", - "print(len(df_result_merge_text))\n", - "df_result_merge_text.head(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Fy96MPEvyXCc" - }, - "outputs": [], - "source": [ - "df_result_merge_text['Comportamento agressivo?'] = df_result_merge_text['Comportamento agressivo?'].str.lower()\n", - "#df_result_merge_text['tweet_text_y'] = df_result_merge_text['tweet_text_y'].str.lower()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3DxHVYbVpF6l" - }, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "l0iDtJtBns0Q", - "outputId": "ab71619b-21ec-4273-f6ba-3821eba54b7f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Dimensões:\n", - "\n", - "Shape: (834, 46)\n", - "\n", - "Quantidade de dados faltantes:\n", - "\n", - "Comportamento agressivo? 0\n", - "tweet_author 0\n", - "tweet_text_y 0\n", - "dtype: int64\n" - ] - } - ], - "source": [ - "df_result_merge_text_analise=df_result_merge_text[['Comportamento agressivo?', 'tweet_author', 'tweet_text_y']]\n", - "print(\"\\nDimensões:\\n\")\n", - "print(\"Shape:\", df.shape)\n", - "print(\"\\nQuantidade de dados faltantes:\\n\")\n", - "print(df_result_merge_text_analise.isnull().sum())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 345 - }, - "id": "es3Nmym3vG7o", - "outputId": "990378b8-0d16-4989-c03d-f531ebf7c311" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tamanho dos comentários:\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \"\"\"Entry point for launching an IPython kernel.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes@LucianoHangBr Já demorou muito!, RT @LucianoH...10004
1nãoMaurcio98905595HOSPÍCIO....LOUCA. https://t.co/34BbY21hrQ, . ...7015
2nãoLunVianaRT @jairbolsonaro: - RIO DE JANEIRO / RJ: O @g...11420
3simfelipeleixas@RachelSherazade Vc chama isso de jornalismo? ...2846
4nãoJoseCar41451194RT @BrazilFight: JANAÍNA PASCHOAL\\n\"Jamais um ...11465
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\n", - " " - ], - "text/plain": [ - " Comportamento agressivo? tweet_author \\\n", - "0 não lemathes \n", - "1 não Maurcio98905595 \n", - "2 não LunViana \n", - "3 sim felipeleixas \n", - "4 não JoseCar41451194 \n", - "\n", - " tweet_text_y Tamanho \n", - "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... 10004 \n", - "1 HOSPÍCIO....LOUCA. https://t.co/34BbY21hrQ, . ... 7015 \n", - "2 RT @jairbolsonaro: - RIO DE JANEIRO / RJ: O @g... 11420 \n", - "3 @RachelSherazade Vc chama isso de jornalismo? ... 2846 \n", - "4 RT @BrazilFight: JANAÍNA PASCHOAL\\n\"Jamais um ... 11465 " - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_result_merge_text_analise['Tamanho'] = df_result_merge_text_analise['tweet_text_y'].apply(len)\n", - "print(\"Tamanho dos comentários:\\n\")\n", - "df_result_merge_text_analise.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 476 - }, - "id": "ojUpwQTOvUFn", - "outputId": "9b1b3880-7003-44d3-9ccc-dda01016f064" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Distribuição do tamanho do texto')" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_dist_grafico = plt.figure(figsize=(10,6))\n", - "sns.distplot(df_result_merge_text_analise['Tamanho'], kde=True, bins=50, color=\"green\")\n", - "plt.title('Distribuição do tamanho do texto')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qu-5y7LFvou_", - "outputId": "050b7720-b280-4a8f-da63-c3aa8874048a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total: 834\n", - "Total de sim: 98\n", - "Total de não: 735\n" - ] - } - ], - "source": [ - "#total da likes\n", - "total = df_result_merge_text_analise['Comportamento agressivo?'].count()\n", - "total_sim = (df_result_merge_text_analise['Comportamento agressivo?']=='sim').sum()\n", - "total_nao = (df_result_merge_text_analise['Comportamento agressivo?']=='não').sum()\n", - "print(\"Total:\", total)\n", - "print(\"Total de sim:\", total_sim)\n", - "print(\"Total de não:\", total_nao)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "aOkaDiM60gxw" - }, - "outputs": [], - "source": [ - "texto = df_result_merge_text_analise[['Comportamento agressivo?', 'tweet_author', 'tweet_text_y']]\n", - "texto['Tamanho'] = texto['tweet_text_y'].apply(len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KG8nJnA80xhL" - }, - "outputs": [], - "source": [ - "stopWord = stopwords.words(\"portuguese\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XMAsFIAc09j3" - }, - "outputs": [], - "source": [ - "def remove_puntuacao_stopwords(texto):\n", - "\n", - " remove_puntacao = [word for word in texto.lower() if word not in string.punctuation]\n", - " remove_puntacao = ''.join(remove_puntacao)\n", - " return [word for word in remove_puntacao.split() if word not in stopWord]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 241 - }, - "id": "k0JVUzGr1Iv4", - "outputId": "c4ed3a64-b16c-41d7-db1e-9a14998f02a9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tamanho dos comentários após aplicação do stopword:\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
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\n", - " " - ], - "text/plain": [ - " Comportamento agressivo? tweet_author \\\n", - "0 não lemathes \n", - "1 não Maurcio98905595 \n", - "2 não LunViana \n", - "3 sim felipeleixas \n", - "4 não JoseCar41451194 \n", - "\n", - " tweet_text_y Tamanho \n", - "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", - "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", - "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", - "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", - "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 " - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "texto_preprocessado = texto.copy()\n", - "texto_preprocessado['tweet_text_y'] = texto['tweet_text_y'].apply(remove_puntuacao_stopwords)\n", - "texto_preprocessado['Tamanho'] = texto_preprocessado['tweet_text_y'].apply(len)\n", - "print(\"Tamanho dos comentários após aplicação do stopword:\\n\")\n", - "texto_preprocessado.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 367 - }, - "id": "3TSR5jYI1XCy", - "outputId": "d1fa1480-e3ce-472e-f388-c689fe361aa6" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Distribuição do tamanho do texto após aplicação de STOPWORD')" - ] - }, - "execution_count": 156, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_dist_grafico_processado = plt.figure(figsize=(8,4))\n", - "sns.distplot(texto_preprocessado['Tamanho'], kde=True, bins=50, color=\"blue\")\n", - "plt.title('Distribuição do tamanho do texto após aplicação de STOPWORD')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dW1fnIfj1lsj" - }, - "outputs": [], - "source": [ - "def grafico_frequencia(data):\n", - " plt.figure(figsize=(10,5))\n", - " FreqDist(np.concatenate(data.tweet_text_y.reset_index(drop=True))).plot(25, cumulative=False, color=\"green\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423 - }, - "id": "1A0s6yxv1wye", - "outputId": "9b082065-e27b-408f-f660-d5aa0c42dfec" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gráfico de frequência de Comportamento agressivo = sim:\n", - "\n" - ] - }, - { - "data": { - "image/png": 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Z2Rwz2z13GW6m6O6bY6L2WmCqmeXGxu0NrIq3V8XEjXh+CrAhv7yfx5S9/P0+u727xLURERGRSjLcRO0M4NPAvYSuzKVA82APMLM9zGxqvD0JeDPwJCFhy80YPQO4Lt6+Ph4Tz98es8zrgffHWaH7AwcDS4ZZ75KbWDORqROn0tndyabtm0pdHREREakgg876zHH3/YuIPQe4Mo5TqwKucfcbzOwJ4NdmdjHwEKErk3j9CzNbDmwkzPTE3R83s2uAJ4BO4ByvkBmfObMaZrF5x2ZWb1vN9Prppa6OiIiIVIhhJWpmdnp/5e5+1UCPcfdHgSP6KV9BP7M23X0H8N4BYn0F+Mpw6lqOZjfO5ukNT7N622peNfNVpa6OiIiIVIhhJWrAkXm3JwInAA8CAyZq0ktrqYmIiEgxhtv1+S/5x3Hs2a8zqdEYNKuhd0KBiIiIyHANdzJBoVagmHFr45Ja1ERERKQYwx2jtoCwqwCEzdhfCVyTVaXGGiVqIiIiUozhjlH7dt7tTmClxx0EZGi5RE1dnyIiIjISw+r69LA5+1PAZGAa0JFlpcaa3KK3alETERGRkRhWomZmpxAWmX0vcApwv5mdPPijJEddnyIiIlKM4XZ9fh440t3XQth1ALgNuHbQRwkAe9TvgWGsa11HZ3cnNVXD/bWLiIjIeDbcWZ9VuSQt2jCCx457tdW1TK+fjuOsb1tf6uqIiIhIhRhusnWzmd1iZmea2ZnAH4Abs6vW2KPuTxERERmpQRM1MzvIzF7v7p8GfgocHi9/Ai4dhfqNGUrUREREZKSGGiz1PeBzAO7+O+B3AGb2N/HcOzKt3RjSs0THNi3RISIiIsMzVNfnLHd/rLAwlu2XSY3GqNw2UmpRExERkeEaKlGbOsi5SWlWZKxT16eIiIiM1FCJWrOZfbSw0Mw+AizNpkpjU0+i1qpETURERIZnqDFqnwB+b2YfoDcxmwfUAe/OsmJjTa7rU2PUREREZLgGTdTcfQ3wOjM7HjgsFv/B3W/PvGZjjLo+RUREZKSGtUS+u98B3JFxXcY0JWoiIiIyUtpdYJRMr59OtVWzaccm2jvbS10dERERqQBK1EZJlVUxs2EmAGtb1w5xbxERERElaqNK3Z8iIiIyEkrURpESNRERERkJJWqjaFZjXKKjVUt0iIiIyNCUqI2i2Q1qURMREZHhU6I2itT1KSIiIiOhRG0UqetTRERERkKJ2ihSi5qIiIiMhBK1UaRETUREREZCidooUqImIiIiI6FEbRRNmTCFuuo6tnVso7WjtdTVERERkTKnRG0UmVlPq5omFIiIiMhQlKiNMnV/ioiIyHApURtlsxriEh3b1KImIiIig1OiNsrUoiYiIiLDpURtlClRExERkeFSojbKNJlAREREhiuzRM3M9jGzO8zsCTN73MzOjeW7m9lCM3smXk+L5WZmPzCz5Wb2qJm9Ji/WGfH+z5jZGVnVeTTkxqipRU1ERESGkmWLWifwKXc/FDgGOMfMDgU+Cyxy94OBRfEY4K3AwfFyNnAJhMQOuBA4GjgKuDCX3FUidX2KiIjIcGWWqLn7S+7+YLzdAjwJ7AWcBFwZ73Yl8K54+yTgKg/uA6aa2RzgLcBCd9/o7puAhcCJWdU7a0rUREREZLjM3bN/ErP9gLuAw4Dn3X1qLDdgk7tPNbMbgK+7++J4bhHwGeA4YKK7XxzLLwC2u/u3C57jbEJLHHPmzGlasGBB5j9XW1sb9fX1I3tMZxtvuPkNTKiawOK3Lib8CpLFHPI5FTPVmFnFVUzFLPe4ijk+Y2YVdzzHLDRv3ryl7j6v35PunukFaASWAu+Jx5sLzm+K1zcAx+aVLwLmAecDX8grvwA4f7DnbGpq8tHQ3Nxc1OMavtLgXIRv3r45tZiDUcz0VUpdFXN8xswqrmKOz5hZxR3PMQsBzT5ATpPprE8zqwX+F7ja3X8Xi9fELk3i9dpYvgrYJ+/he8eygcorlro/RUREZDiynPVpwGXAk+7+n3mnrgdyMzfPAK7LKz89zv48Btji7i8BtwDzzWxanEQwP5ZVLC3RISIiIsNRk2Hs1wMfAh4zs4dj2b8DXweuMbOzgJXAKfHcjcDbgOVAG/BhAHffaGZfBh6I9/uSu2/MsN6Zm9WoJTpERERkaJklah4mBdgAp0/o5/4OnDNArMuBy9OrXWnNblDXp4iIiAxNOxOUgMaoiYiIyHAoUSuBXNfnmm0aoyYiIiIDU6JWAj0taq1qURMREZGBKVErAXV9ioiIyHAoUSuB3Mbs6voUERGRwShRK4GeMWqta+j27hLXRkRERMqVErUSmFgzkakTp9LZ3cnG7RW9JJyIiIhkSIlaiWicmoiIiAxFiVqJaJyaiIiIDEWJWomoRU1ERESGokStRJSoiYiIyFCUqJVIT9dnq7o+RUREpH9K1EpELWoiIiIyFCVqJaJETURERIaiRK1ElKiJiIjIUJSolUj+7gQiIiIi/VGiViJ71O+BYaxrXUdnd2epqyMiIiJlSIlaidRW1zKjfgaOs651XamrIyIiImVIiVoJqftTREREBqNErYQ0oUBEREQGo0SthJSoiYiIyGCUqJXQ7AYlaiIiIjIwJWol1DNGbZvGqImIiMiulKiVUE/XZ6ta1ERERGRXStRKSGPUREREZDBK1EpoVoO6PkVERGRgStRKSC1qIiIiMhglaiU0vX461VbNph2baO9sL3V1REREpMwoUSuhKqvS7gQiIiIyICVqJaZxaiIiIjIQJWolpnFqIiIiMhAlaiWmRE1EREQGokStxHq6PjVGTURERAooUSsxtaiJiIjIQJSolZgSNRERERlIZomamV1uZmvNbFle2e5mttDMnonX02K5mdkPzGy5mT1qZq/Je8wZ8f7PmNkZWdW3VLQ8h4iIiAwkyxa1nwMnFpR9Fljk7gcDi+IxwFuBg+PlbOASCIkdcCFwNHAUcGEuuRsr1KImIiIiA8ksUXP3u4CNBcUnAVfG21cC78orv8qD+4CpZjYHeAuw0N03uvsmYCG7Jn8VTYmaiIiIDMTcPbvgZvsBN7j7YfF4s7tPjbcN2OTuU83sBuDr7r44nlsEfAY4Dpjo7hfH8guA7e7+7X6e62xCaxxz5sxpWrBgQWY/V05bWxv19fWJYrg7r7/p9XR0d3D3iXfjHZ44ZqE06qmY2cdVTMUs97iKOT5jZhV3PMcsNG/evKXuPq/fk+6e2QXYD1iWd7y54PymeH0DcGxe+SJgHnA+8IW88guA84d63qamJh8Nzc3NqcR52Xdf5lyEP7vx2dRi5lPM9FVKXRVzfMbMKq5ijs+YWcUdzzELAc0+QE4z2rM+18QuTeL12li+Ctgn7357x7KByscUdX+KiIhIf0Y7UbseyM3cPAO4Lq/89Dj78xhgi7u/BNwCzDezaXESwfxYNqYoURMREZH+1GQV2Mz+hzDGbIaZvUCYvfl14BozOwtYCZwS734j8DZgOdAGfBjA3Tea2ZeBB+L9vuTuhRMUKl7+xuz7Vu1b4tqIiIhIucgsUXP3Uwc4dUI/93XgnAHiXA5cnmLVyk6fFrXdSlwZERERKRvamaAMqOtTRERE+qNErQz0JGqtStRERESklxK1MpA/Rk1EREQkR4laGVDXp4iIiPRHiVoZyG3Mvnrb6tzCviIiIiJK1MpBY10jDbUNtHe109rZWurqiIiISJlQolYmct2f69vXl7gmIiIiUi6UqJWJXKK2oX1DiWsiIiIi5UKJWplQoiYiIiKFlKiVidwSHRvbx9wOWSIiIlIkJWplQi1qIiIiUkiJWpnQZAIREREppEStTOTWUlPXp4iIiOQoUSsTuRa1e9fey9E/O5oLbr+Au1beRUdXR4lrJiIiIqVSU+oKSHDE7CM46ZCT+MOf/8CSVUtYsmoJF999MY11jRy/3/G8+YA3M//A+bx8+ssxs1JXV0REREaBErUyUVtdy/+9//9YfP9itu6+lYXPLuTWFbfyxLonWPDnBSz48wIA9tltH+YfOJ/5B87nhP1PYHr99BLXXERERLKiRK3MTKqZxLEHH8vbDn4bAC9sfYHbVtzGrc/eysIVC/nr1r9y2UOXcdlDl2EYTXs2Mf+A+bz5wDfzun1eR111XYl/AhEREUmLErUyt/due3Pmq8/kzFefSbd388jqR3qStrufv5vmF5tpfrGZry7+Kg21Dbxxvzcy/4DQ4vaKGa9QN6mIiEgFU6JWQaqsiiPmHMERc47gM8d+hradbdy18q6ebtJla5dx4zM3cuMzNwKw1+S9mH/gfGZ2zuT5+ueZ3TibWY2zmN04m/ra+hL/NCIiIjIUJWoVrL62nhMPOpETDzoRgBdbXuzTTbqqZRVXPHxFuPOyvo+dXDe5T+I2u2F23+N4mdkwU92pIiIiJaJEbQzZc/KenD73dE6fezrd3s1jax5j4YqF3Pv0vXRN6mL1ttU9l5aOFlo2tvDMxmeGjLv7pN37JG+zGmbRsbmD+7ruY7cJuw14mVgzUV2vIiIiCShRG6OqrIq5s+cyd/Zclk5YSlNTU885d2fzjs2saV3TJ3lbs20Nq1tX9ylb27qWjds3snH7Rp5Y90TfJ3lq8DrUVNX0SdymTJgyaGK35sU1/LXhr9RU1VBTVUNtVW3P7ZqqGmqra/s9l1+ef67KtEygiIhUNiVq45CZMW3SNKZNmsYrZrxi0Pt2dXexYfuGvsncttUs+8syGqY1sLV964CX9q72niRv2B5M+MMVmFA1gTmL57Dn5D3DpXHP3tt5l90m7KbWPxERKTtK1GRQ1VXVzGyYycyGmRw+6/Ce8qUT+7bS9ae9s52Wjha27NgyaEK3tX0rWzu28sKaF5g8ZTI7u3fS2d1JZ3cnO7t6b3d2dw54rrB8Z/fOUIfudp7b/BzPbX5u0LrW19b3Td4KEro5k0Oy11jXmPh3KiIiMlxK1CQzE2omMKFmAjPqZwzr/kuXDp38jUS3d3PPknuYc/AcXmx5cdBL685Wlm9czvKNyweNObluMnMmz6G2q5Y5T8zp7bqtG7hLt/BSW12b2s8oIiJjmxI1GbOqrIr6mnoO2v0gDtr9oAHv5+60dLTwUstLuyZx2/oet3S00LKhBYDHNz9eVL0m1kzsN4Gr3l7N3Ja57LXbXuw5eU/2mhyuZ9TPULesiMg4pURNxj0z60mWDplxyID3y03CWL1tNfc/cj977r/n0F26BZct7VvY0bmDHZ07WNu6dpfn+P3zv9+lrK66jjmNc3ZJ4HquY7m6ZUVExh4laiLDlD8Jo21aG00Hjryb1t3Z3rm9N3GL4/c279jMkieXUDO1hhe3vciqrat4seVFVrWsYvOOzazcspKVW1YOGnty3eRdkrkt67ZwS9st1FTVUG3VVFdVU23V4TjeHqis8DG5shWbV1C/rp6GugYa6xppqG2grrpOrX4iIhlQoiYyisyM+tp66mvrmd04u8+5/bbv1+8YvbadbX26X/OTuPzrlo4Wnlr/FE+tL1g35c8Z/CCL+x5WW3VI2uoaaKhtGPx2P2XPr3me9cvXU11VTZVV9SSG+berLR4PcTv3uJadLWzfuZ0JNRO0VIuIVCwlaiJlrr52eOPsNu/Y3Ju8xWRuxV9XMHPWTDq7O+nyLrq6u+jyrnAcb+fKB71PXtnGrRvxGmdbxzZad7ayrWMbnd2dbGnfwpb2LcX/oA8U/9AB3RKuaqpqmFAdJreM6LqgbP2a9dy47UbMDMOosqqe20NdD3Tfvz7/Vx6tepTa6lrqquuoraod8HZddR211bV9buffr7aqVi2bImOMEjWRMSC/W/awmYf1lKc9k3agmB1dHbR2tPYkbsO9vW1nuF63cR0Nkxvo8i66vbsnMRzO7a7ueFxwu31nO53eSXtXe8/SLa07W5P/ArJooXwsvVC5RZ9xqLol3ZZE73ZqFtb0tHRWWdUul1yLZp+yQe67o20H05ZNGzw5HWHCuvLFlfxl0l9S/dmfffFZnpv0XE/dzaz3dl4iPpKypzY/hb2UXtJvGGu3r2X1ttV9FgHPDWOoqapRIl+BlKiJSGJ11XXUTapj2qRpRT0+y4TS3dnZvZP2znbau9oTXa9ctZLZs2fj7jhOt3f33B7Odbd3708byhQAACAASURBVFK2dt1apk6fSkdXBzu7dobr7p19bufO5d/u7375aw4C0JXqrzS7mCNYE3vYUl48O7OYi4e+y4gtGvhUlVXtkrz1HFdVD5jgbW/bTv2D9alWc0fbDqY8MmXonWds+DvUrHphFfd03tMnMc5PjvsrGyqxfm7NczSR7vvTSChRE5ExzcxCIlldx2QmJ4o1Wi2UxcolpTu7dvLgQw9yxBFHpBI358GHHmTu3Lk9rZbd3t3vJdeyOeD5vFbRJ556gv0P2n/IJHU4SWtHVwcdXR1s2LiBadOK+9IwkI2bNjJ16tSeBD2XpPfczkvch1u2rXUbk+onFZX4D3Tf9o52qmqqeoYu5Cfvud977vc0YglGNgxocwYxnxj6LiMxoWoC577t3HSDjoASNRGRMSI/Ka2vqU99yZaGmgamTJySasy6tXU07V++ye9YiplLGAuTt/zj/hK8zu5OnnzqSV75ilemVk/HeeLJJzjo5QcNustMf+UDndvZtZPVa1azx8w9Bk+mR5hst25NYchEAkrURERExgEz65kZPYEJI3ps1eoqmvZKN6msXl1N08sqI/ktJc1ZFxERESlTFZOomdmJZva0mS03s8+Wuj4iIiIiWauIRM3MqoH/At4KHAqcamaHlrZWIiIiItmqiEQNOApY7u4r3L0D+DVwUonrJCIiIpIpc/dS12FIZnYycKK7fyQefwg42t0/nnefs4GzAebMmdO0YMGCzOvV1tZGfX2668ooZvnHzCquYipmucdVzPEZM6u44zlmoXnz5i1193n9nnT3sr8AJwM/yzv+EPCjge7f1NTko6G5uVkxx2HMrOIqpmKWe1zFHJ8xs4o7nmMWApp9gJymUro+VwH75B3vHctERERExqxKSdQeAA42s/3NrA54P3B9ieskIiIikqmKWPDW3TvN7OPALUA1cLm7P17iaomIiIhkqiISNQB3vxG4sdT1EBERERktFTHrc6TMbB2wchSeagawXjHHXcys4iqmYpZ7XMUcnzGzijueYxba19336O/EmEzURouZNftA02kVc8zGzCquYipmucdVzPEZM6u44znmSFTKZAIRERGRcUeJmoiIiEiZUqKWzKWKOS5jZhVXMRWz3OMq5viMmVXc8Rxz2DRGTURERKRMqUVNREREpEwpURMREREpU0rURERERMpUxexMICMT90R9eTx82t13lrI+IiJZMrPzBjvv7v85WnURSZMStREys1+4+4eGKisibmqJlZkdB1wJPAcYsI+ZneHudyWpY6Uws3rgU8DL3P2jZnYwcIi735BC7MOAQ4GJuTJ3vypBvInAWcCrCmL+Y4KYb+8n3peKjRdjVkTib2azgK8Ce7r7W83sUOC17n5ZCrF3I+890903Jo2ZJjP7KvBNd98cj6cBn3L3L5S2ZoGZLXL3E8zsG+7+mQyeYh5wJHB9PH4HsAR4JklQMzsc2I++f/vfJYz5TuAN8fCP7r4gSby8uDPp+3//fBpx8+K/wt2fSjNmWsxsEuE9/+mU4k0EPgYcCziwGLjE3XekEX9EddGsz5Exswfd/TV5x9XAY+5+aIKYx1GQWAFFJ1ZmthQ4LfeCNbOXA//j7k1FxFrs7seaWQvhxdpzCnB3363IOv6wIF4f7v6vxcSNsX8DLAVOd/fDYuJ2r7u/utiYMe6FwHGERO1G4K3AYnc/OUHM3wJPAacBXwI+ADzp7ucWGe8nQD1wPPAz4GRgibuflaCOx5Hi6zMv7sHA19g18T0gQcybgCuAz7v7XDOrAR5y979JEPOfgC8CO+h9zXrCer4cuASYFV+jhwPvdPeLE8R8yN2PKCjr835VRMxvAhcD24GbgcOBT7r7L4uI9QTwEeAywuvd8s+7+4PF1jPGvwt4u7u3xOPJwB/c/Q2DP3LQmJcTfubHge7eqib6IvU14Cjg6lh0KvCAu/97gpjvBL4D7AmsBfYlvI+8qtiYAzzP8+7+soQx9gZ+SG8CdDdwrru/kCDmO4BvA3Xuvr+ZvRr4kru/M0HMa4AWIPdaPw2Y6u7vLTZm0dxdl2FcgM8BrUAXsDVeWoANwNcSxl5KaPHJHb8cWJog3qPDKSvx7/OMwS4JYzfH64fyyh5Joc6PEcZ1PhKPZwELE8Z8KP/vA9QC9yX92+ddNwJ3J6xjqq/PvDiLgROARwkfLBcR3lyTxHygn7/9wwljPgPMSPrzFsT8I+HDOr+eyxLGfBSYkHc8CXg8YcyH4/W7CQnWlGL/lwhfGm6K75u3A3fkXW5P4Xf6dMHPP4HQ+psk5hNp/t3z/k5VecfVSd+fgUeA6XnvJ8cDlxUZ6wcDXH4IbE3h518IfJjQQlkDnJnC++jS+NrM/396LO2/fRavh+Fc1PU5TO7+NTP7OvCMux+Ucvhaz2uudfc/m1ltgnhLzexn9H4T+ADQnKSCOWk1rbv7lWnUZwAdsRncAczsQKA9hbjb3b3bzDpjN9haQutSErkuxM2xW3U1MDNJHeN1m5ntSfgiMSdBPEj/9Zkzyd0XmZm5+0rgotga/B8JYraa2XR6//bHAFsS1vNZoC1hjEL17r7ErE+jUmfCmL8EFpnZFfH4w4SW0CRynxFvB37r7lsK6jxs7n4tcK2ZXeDuX05Yr/5cBSwxs9/H43cBP08Y809mdqi7P5EwTqGpQK7rfEoK8Xa6+wYzqzKzKne/w8y+V2SsDxOGjvT3nnlq8VXssYe7X5F3/HMz+0TCmDv7eW0m7S580MyOcff7AMzsaFL6HB0pJWoj4O5uZveY2ZHu/kCKodNOrP4fcA6Q6z68G/hxgngDNq0TxkIVE+977v4JM1tAP/9QnqDJmtAyczNhbN7VwOsJbz5JNZvZVOC/Cd/gtgF/Shjz0jiW6AuEsTWNwAUJ4t0Q6/gt4EHC7/ZnCevYnFHi325mVcAzZvZxYBXh50/iPMLv8UAzuwfYg9CSk8TngHvN7H7yPrw8Qfc8sD5+gcgllCcDLyWqZWjtvwTIdfF/2d1vSRjzBjN7ivAF4J/NbA9CF3ASxwF9ErXc+LUkQd39K7Hr+29j0Yfd/aEkMQnJ35/MbDXhb58b8nF4gphfAx4ysztivDcAn01Yz81m1gjcBVxtZmsJPUDFeIDQuntv4Qkzu6j4KvbYYGYfBP4nHp9K+EKZxONmdhpQHYdU/CuwS/1HqInwf59rjHgZ8LSZPUby18CIaIzaCMU3rYOAlYR/hMT/uGY2gZBYHRuL7gZ+7O4jbgWKY+Yed/dXFFufAeI+ArwJuM3djzCz44EPepFjn8ysyd2Xmtkb+zvv7n9MUF1iq8oxhL/Pfe6+Pkm8fuLvB+zm7o8miFEFnOzu16RVr4L4E4CJ7p6oRSnN12dB3CMJyf5Uwgf3bsC3ct9gE8StAQ4h/O0TT3wwsyWEbtrH6B2nlKhV2MwOIGxL8zpgE/AX4AOxZbHYmBcCpxBaan5DaAFbU2y8vLi7A1vcvSuO99zN3VcXEWci0EDo9jyO3jFquwE3p/2elQYzW05I/gv/9kX/nWLcOYSJDxDGkI7491kQr4GQTFcRvkhNAa529xEnQPHvvcPd025FzsXfl9CN+lrCF5V7gX8ttncmxqwHPg/MJ7yubiF8USn6S0Ws54CSvgZGVBclaiMz0B+v2D9aFomVmV0H/EuSF34/MZvdfV5M2I6IXYCPuPvcFJ9jGrBPkuQnxtnl23mSb+xmNuhgbE8wCDr3ey328Xlx3uTut5vZe/o77wlnqaUtvu6/4e7npxRvsJ/fCcnLYnfvKiL2LoP0k8j/2eMHbJXHAfApxT8ceB/wD8AL7v53CWLVAv9M3gxF4CfFJL9mdi7wCUKr/Cril1zCmLVL3f2/iq1nVszsT+7+2pRjLgB+BVzv7sW2euXHqyZ8gT4+ceUyFut6lbt/oNR1GUps8X7B3dstTKg6nFD3zaNdF3V9jlDaWXT8lvq0mb0sxcRqGqEpeAl5zd8JuxNzTet3k7xpvYeZ3Qm8k/BaXAqsNbN73H3QNZEGiDWRMONxRkz68r+x75Wgmt8Z5JwTWhqLdZuZnU9oAcn/W4106Yc3EFoq3kE/s3OBohM1M3s9oTt5X/ouUVD0rMf4uj926HsO2xvp/fn7M53QvfzmImLfZGZnAwvo2/VZ1PIc+T97Gh/U/VhLGOu4gWTjHSF0pdbSO3TiQ7HsIyMN5O7fB75vZv8BfM/dt5rZBcBrSD6EICsPmdmv2PVvn+SLz7cJifTXzewB4NfADcW2/sTXU7eZTUnaeg5htqO7n5Lr4ss/RcLeo1jXfc2szt07Uqhrv0Nn8p4vyWfe/wLzzOwgQuv3dYQE+20JYhZFLWplwMK08iMIa/4kTqyy6E40s88TBuauBj5Igqb1grgPxa7UjxBa0y40s0eLeTMY4Bs7hBm6/+3uP0pS1yyY2V/of4zeiJIgM/tUjGN51+Rie4LFPmN3/ycJiXRPi1QKf/tLCAn0b+n7us+k9c/MLiumqz7+jQp5kkQ1i5/dzD5G6PrcI8a9Jukg+P5azZO2pOf+v2Oy+mVC4vIf7n50krpmwXonZuRzT7A8R17sasKXvI8CJ3qRSx3FWNcRPkMW0vf1NOJxlGY2x91fSrv3KC/+VcArCWNJ8+s64veovM+69wCz6R1Heyqwxt0/maCeD7r7a8zs3wgTyX6Yduv6cKlFrTwkGTy+i6TjuwZQA9xK7/iX3yT9oM7FjeM1TiGMMSha3jf2f3H3H6ZQtz7M7PQBnrfoBW8Ja4jlL6p4N/CTIuLkBuEfQhj7ch0hWcst+pnEFne/KWGM/kwktPrkt0gmbf2bAlxI3666L7n7lmLHU7r7/sXWZxCp/+yEGcifcPeHk1SsQJeZHejuz0LP2LoRdx8XxozXbyd8gfqDmRW9flyW3D2NSUi7sDAr/R2ElrXXkHx27u/Y9bVTVCuMu78Ur1fGZO1gd78t1jmNnOHZeKkCJicJlPusM7PvFAwhWWBmSSc87TSzU4HT6W2pT2O2+4ipRW0MsrAkwQ8J31rqCOv0tCb5xpYXO7XxLzHeewmJ6mJ3/1j8IPiWu/9DwrivY9fVxJMkVLlFenMmEtYAe9CTLXh7DaHFL7f45WnAFHc/pch4WSz6+XXCa+h39O3+SbRAaRbM7H+BZfR+8H0ImOvu/Y7dG2bMwnFadwI/TTpJoRKY2QmEBYRXEBL/fQmzKe9IEPMGQov3mwlJynbCgPrUxrsmZWb/5u7ftAEW5i6mpSov9jWENfRuJnzp/aO7dw/+qCFjnhu/qA5aNsKYHwXOBnZ39wMtzKb8SbFjffuJ3wjg7ttSiPUk4X1vRTzeH7jR3V+ZIOahhBUU/uTu/xNjnuLu30ha3xHXRYla6aWdWMVvEu8ndH/MI3wjeLm7fy6Fus4G3hvjT04yXiErZvYL4EDgYXq/vXuSN9cBnmcq8Gt3PzFBjCe8YFeL/spGEO9p4HCPMzItzNh81N0PSVDH/j6U3d2TjM3LX6H89bEojRXKH/aCHSj6KxthzJ8RvknnJ39d7j7icVp5MQ8Avk+YmeyEMVqfcPf+ullLKr6Gcq+fpz35bN964ETCgqTPxBb1v3H3WxNWNTVm9g53X2BmZ/R33pPN+H0LYfB/0pbJ/Ji77ECRtJvOzB4mJJT35+KY2WOeYJePGOMw4BfA7rFoPWEXmccTxDyRMI4s/wvF2UlfU1YmW+ep67M8/Ih+EqskAd19uZlVxzeDK8zsIcJ6UEXpZ/zLR5OOf4lxU9/rkvA7PNSz/xbSCiTtFkt7UcVUF/2M42iud/fvJqjTQK4gDM7NbcnywVhWzID/nO1mdqy7L4aeiRDbh3jMUI4saO253cLs5yR+BfwXYcV/CP//vwbKYpyWDTB7GDjIzBKNpfOw7MPv8o5fIvkacqnyuPdmkoRsELcD55hZGjNpTyW0wu9vZtfnnZpM74K6xWp39w6Li8haWPYmjffUS4Hzcq2yFmZU/jdhqZqiuPvNscUvt3rCUyl8oTiOMtkzW4lamUg5sWqL3wQetrBX30uE8QBJZDH+BcI3q6eAt5C312XCmMsIA0tTffMvmGFURRhflnQNtFQXVfSUF/30MEvrVCCLRC2LFcr/GbgyjlWDsEZZv60iI5DFOK16d/9F3vEvzezTCWOmKTcmZybhA3QR4cPqeMK6V2W13EtWLCzw+xl23Y82SWtyajNpCX+Ll4AZ9J2d3kLYqiqJP5rZvwOTzOzNhLG0aWwe35Dfde7ud1pYpqZocXjCP5E3PMHMkg5P+A4w3wv2zCa8Z48qdX2WgTiu6O8IK8ivJvzjnVnsmI04AHQNoRv1k4QZmj929+Xp1Dg91jvrMzcTrJawN+UxCWLeQVidfQl9x1QlmapdOJu2E1iZpJsuxiybRRUHYmbfJXywFC4hknQT7UWEFrT8Fco/nGQMTOymO5nQ9T2VsH2Uu/uXEsTMH6cFYexjUeO0LCwmCuHDfxOhFc0J4z6npTE8IU1mdith792X4vEc4Ofu/pbS1mx0xJ//N8D5hPFKZwDr3P0zCWKmPpM2CxYW5D6LvovI/ixpT0Vs7X+Q8CUdQkt6k7u/e+BHDRkzi+EJu6w+0F/ZaFCiVgYqKbFKm5ktcfejYrL6MUKiusSTLX2QyW4H41WGY9SyWKH8ZmAz4YMgfymRwdbCGyrmRMLehyfE2A8A3/Ui1r2y3uVY+tsw05O87rNgZk/mD8iOH96PJxmkXUnMbKm7N+V/QJvZA+5+5FCPHSTmg8B7C1pory0cYzbCmO8BvkFoAbV48WLHOWfJwhqXX6Tv2NSLPMFCslkkvxaWZumi79Z51QmH5RRFXZ9lIK/VZAfhBZyIZbBAaYbS3usSd/+j9Z1WXk+YoFEUM2uh/7EZZftmmCbPaMXz+LpP1MrZj72TTO4YwFWEmbm5/SlPI7QGvHfARwzAs1nqI0uLzOwWels93wfcVsL6jLZc19lLZvZ24EV6B8EX69PAHWbWp4U2YcxvAu9w96TDRnpYSms89uNAwlCaKsLn0wmEZWqStFRlMTwh9T2zi6VErQxkkFhdRj8LlJab+O18q7tvImwmnEoiaXnTyglvCnsR1iYrqkvN3ROt9VPpzGwW8FVgT3d/q4Vp669198sSxv1BP8VbgGZ3v67IsPea2d+4+2MJqlboMO87C/cOM0u6kGxFLPnh7h83s3fTW89L3f33gz1mjLk4jnf8FKH1dzfCe2sS9wA/pbeF9haS78ywJs0kLcpfl2wi4YtJ0iQVwlJE5xPGEidaliRPqslvnET1iIetHYteLDwt6vosA5byyu9mdr+X4Srf/bGU9rosiJnJtPLxKk5OuAL4vLvPjbO/Hkphmv6lhFlav41F/0DYnHw6sMLdhz2xwHq3u6kBDiaMJ2uH5NvemNkvgR8VzMw9x937XQB5mDFTH1OTlZioH0X4/S5x97UlrlJFs/7XTpzq7iNuoc2L+X3CBKr/I72trvp7nqXunmgwvZktdvc0t49LdXhCXszU98wullrUykPaK7/fYWbfogIWKCW9vS7zZTWtfLya4e7XmNnnANy908zSaKk9HHh9nOmMhW2V7ibs0jDSFrG/T6E+feQlf7X0zsx1Qsv3UwnDZ7HkR+rM7BTgW4QWPwN+aGafdvdrS1qxUWK96929ltD68yfgkx4XVi1S6i20hJa+NsLA/5yku3zkj5mrIrSwpZEzXBi/qCwivaQyteEJebLYM7soStTKQ9qJVa41Lb+lKunm4Vl5H6FuHysoT9INmtW08vGq1cymE5NdCws0J978mfBG2JgXq4GwCnqXmY1oDaSMZsemnvzlyWJMTRY+T0gq10LPchW3AeMiUaP/9e7+h2Tr3aW9dmJWW13lT8DpJKwnVtSOKQU+TGhJr6W36zPp9mlZJL+pbu2YhBK18pBqYpXV4O+MpLXXZb7PEqaVP0ZYW+dGwtInUpxPESZ6HGhm9xAWPS5626w83ySs9XcnobXmDcBX45pKJR+wnvHSKOeT/oDyLFQVdHVuIPmajJUki/XuUl07EbLZ5SPDz5EjPcFOKQPIIvktm1UCNEZtDLJBNqYuXa36N8B4jaL3upRsxO7jQwgJVWpbqZjZnoTxWU8SWtde8BKs/D3aLOxxewshQXsXoWvt8+U2PMHCgtlz6Tvr89Ek64hVEjP7Bv2sd0foDi5qiEYWayea2UJC61/+2mQfcPeid/kws/MGO+/uRQ2yj8tefMtT2NkmL+aThPenPskvoSWw2OQ3sz2zR1wXJWqll3ZiZRlsTJ0VS3Gvy7wxRf0qxUKFY4GZPUr4oPpNrqsupbgfAc4F9ibsy3oMYQPkcuyiT5X1LvB8LGFczbeB/yi3SUAxUbmf0OINoaXmmHGUqA2292rZrHtn2exx+yvgSEJrOoTdKpYAzwC4e1FLScWk6kDCxKG0Jvxkkfxmtmf2iOuiRK300k6ssvinzUqaM+ry/lnPidf53y7d3T+btL7jUfy9vi9eugkTP65JOhsqJtZHAve5+6vN7BXAV8vxC0XarHdHjq8RNif/lSXcRDsL1v9m3yVZnV0GZtns8nEX8HZ3b4nHk4E/uPsbBn/kkHH7TaoyHmowYrkVCazvYscl+R/VGLXycKC7/0Pe8RfjEhPFymJj6lRlMaMu949uZm8u+Gf6jIXVwJWoFSH+Xr8JfNPCxscXEFZBL3oR4WiHu+8wM8xsgrs/ZWZpj10pV6vM7KeEDei/YWHrq7IZ+2Vm/0wYO3pAbFHNmUxYB2xciOtpvZ3QRZ2/xmXJ19Yq8I+Ebrrv0rvLx5kJY84COvKOO2JZIuWWkA0iiz2zi6JErTyknVjlb0xtwEaS/9OmLcsZdWZmr3f3e+LB6yijD8FKVNCq1gX8WwphXzCzqYS1nxaa2SagUt7EkzoFOBH4trtvtrCHZjltyv4r4Cbga/T9gtOScOmcSrOAsGPMY6S3OGsWvkTYk3UTkNtT9tuEBK5YVwFLLOzNacBJwM8T1rOSfIjwufFxwjqn+xDWehx16vosA2b2akK3Z5/Eyt0TratkZrsBuPvWxJWsIGbWBFxO7+9zE/CP5TZQu1KY2f2Els/fEsapJVlDaqDneCPh73Wzu3cMdX+R0VAp3bz9dcml0U0X11L7W+KMfHd/KEk8KY5a1MqAuz8MzE0rsTKzcwnjFVqA/47/bJ9191sTV7YCuPtSwu9zSjwuu9muFeZ0d386yycop6nwInluMrP5FfDeWWVm0wpa1NL4fO8itCQ65d2imDoroz2zlaiVgQwSq3909++b2VsI2/F8iDCwvtzfbBIxsw+6+y8Lp5Vb3KGgDMeVVIrVZvafVMByLyIpuw/4vYV9iXfSO0Nx1JdoGMJ3gD+ZWW47tvcCX0kSMH4ufRT4X8LP/Uszu9Tdf5ioppWjbPbMVqJWHtJOrCxevw24yt0ft1y2MrY1xOtxvYl6Bi4nzErOrW33IcIXizE/O1PGvf8krHH3mJfxOCF3vyouJ5Fb2uY9KaxTdhZwtLu3Qs9SLX8iTFoYD9Le2rFoStTKQ9qJ1VIzuxXYH/hcnFY95put3f2n8bqo9X1kQGnPShapFH8FlpVzkpYTE7PUFpElfC7ltyR10ftZNR6UzZ7ZStTKQ9qJ1VnAq4EV7t5mYZ/GctyeJhNxKvXFhJmzNxM2//6ku/+ypBWrXGW/3ItIRlYAd5rZTfT9sB4PwyiuAO6Psz4h7KBxWQnrM9rKZs9szfosA3H8Qy6x2hwTq73c/dEhHloY5zWDnR8vsx5zi/ua2bsJy4CcB9zl7nNLXLWKZGZzCVP1p8SiTYSlAEb0+hSpNGZ2YX/l46XVPs6g79k/VLM+S0OJWgmlnViZ2R2Dhxv7W/MAmNkydz/MzH4GXOvuN5vZI0rUipM3OaMxXm8DtgBL44xlERmjzGwmMDF3nHRHkkphZbRnthK1ElJilQ0z+zqhmX47cBQwFbih3PZRrBRxz795hD3/jNBK+Shhtfbfuvs3S1c7kfSZ2ffc/RNmtoB+9g9293eWoFqjyszeSZhNuiewlrDR+VPu/qqSVmyUpL21Y6K6KFEbe8yslrA7Qe6bwJ3AT919Z8kqNcriOkJb3L3LzBqAye6+utT1qkRxz7+3ufu2eNwI/IGwsv5Sdz+0lPUTSZuZNbn7UjM7H3ig4PRkd7+hFPUaTWb2CGE81m1xX9rjgQ+6+1klrtqosDLaM1vb6pQBM6s1s381s2vj5eMx2SrWJUAT8ON4aYpl44KZ1RP2Kcz9zHvSd0CojMxM8gZSE9aTmuXu2wvKRcaEuGg2wGnABnf/Y1yUeU/CXrfjwU5330BYTLfK3e9gfL2PbjezY3MHpZxEpVmf5eESwhY9P47HH4plHyky3pEF47Fuj9+OxosrCIsUvi4eryJsfzTmvwVn5GrC7K/r4vE7gF/Flso0lwMQKTcnA9ea2WmErZROB+aXtkqjZnNsPb8LuNrM1hLGp44XZbNnthK18pB2YtVlZge6+7MAZnYAJV5ZeZQd6O7vM7NTAeISJeNp/Z9UufuX4/IEudlf/8/dm+PtD5SoWiKZc/cVZvZ+4P+A54H5sSV5PHgEaCOszv8BwqzvxkEfMYakvbVjEkrUykPaidWnCYv1rSB8E9iXcbSOGtBhZpOIg4DN7EDURZdITMyah7yjyBhgZo/RdxLB7kA1oWWZStioPQXHu3s3YU3PKyFsUl/aKo2ectozW4laechPrCDMpis6sXL3RWZ2MHBILHra3cdTonIhYaHbfczsakJL0JklrZGIVJK/L3UFSsXM/pkwxvfAgsRsMnBPaWpVEmWzZ7YStfJwD/BT4ARgM3ALYU+1opjZRMI/2rGEb4V3m9lP3H1HCnUta3Hx4GmEfSiPIbQonuvu60taMRGpGO6+stR1KKFfATcBXwM+m1fe4u4bS1OlkiibPbO1PEcZMLNrgK2EQdsQZhpNdff3JojXAuS2TEoUr9KYLIdIXAAABRRJREFUWbO7j6fZSSIikiIzuwLYi7C141xC1/ed7t406nVRolZ6ZvZE4VpU/ZWVKl6liQvergd+A7TmysfZt0ERESlSWls7pkFdn+XhQTM7xt3vAzCzo0k2cDvteJXmfYQu348VlB9QgrqIiEiF6GdrxwNKvWiAWtRKKG9mUS1h4P/z8XhfwlYdI2oBSztepYozPvuM0QN+Mo6m1YuISBHKcWtHJWolZGb7DnZ+pANa045XqQYY8zfF3U8pXa1ERERGTonaGGNm1cDj7v6KUtelVMb7GD0REUmmnPbM1l6fY4y7dwFPm9nLSl2XEnrQzI7JHYzDMXoiIpJM2eyZrRa1McjM7gKOAJbQd9bjO0tWqVFkZk/SO0YP4GXA00AnYYzBeFhVXEREimRmjxRs7dhv2WjQrM+x6YJSV6DETix1BUREpKKVzZ7ZalETERERyWNmJxD2+uyztaO7DzYrNBMaozaGmNnieN1iZlvzLi1mtrXU9RMREakQua0du4GN8XbRWzsmoRY1ERERkTxpb+2YqC5K1MYuM5sJTMwdu/vzg9xdREREKK9lntT1OQaZ2TvN7BngL8AfgeeAm0paKRERkcpRNss8adbn2PRl4BjgNnc/wsyOBz5Y4jqJiIiUtYKtGO81sz5bMZaiTkrUxqad7r7BzKrMrMrd7zCz75W6UiIiImXu70tdgUJK1MamzWbWSNiM/GozW0vewrciIiKyq3LcE1uTCcYgM6sHdgBG6PLcDbja3TeWtGIiIiIyIkrUxhAzW+zux5pZC6FPHUKyBr1rwXzL3X9ckgqKiIjIiChRG0fMbDpwr7sfUuq6iIiIyNCUqI0zZjbH3V8qdT1ERERkaErURERERMqUFrwVERERKVNK1ERERETKlBI1ERnTzOzzZva4mT1qZg/HrWCyeq47zWxeVvFFZPzRgrciMmaZ2WsJK42/xt3bzWwGUFfiaomIDJta1ERkLJsDrHf3dgB3X+/uL5rZf5jZA2a2zMwuNTODnhax75pZs5k9aWZHmtnvzOwZM7s43mc/M3vKzK6O97k2LjLdh5nNN7M/mdmDZvbbuFsIZvZ1M3sitvB9exR/FyJSgZSoichYdiuwj5n92cx+bGZvjOU/cvcj3f0wYBJ99/frcPd5wE+A64BzgMOAM+NahACHAD9291cCW4GP5T9pbLn7AvB37v4aoBk4Lz7+3cCr3P1w4OIMfmYRGUOUqInImOXu24Am4GxgHfAbMzsTON7M7jezx4A3Aa/Ke9j18fox4HF3fym2yK0A9onn/uru98TbvwSOLXjqY4BDgXvM7GHgDGBfYAthe7fLzOw9QFtqP6yIjEkaoyYiY5q7dwF3AnfGxOyfgMOBee7+VzO7CJiY95D2eN2ddzt3nHvPLFyAsvDYgIXufmphfczsKOAE4GTg44REUUSkX2pRE5Exy8wOMbOD84peDTwdb6+P48ZOLiL0y+JEBYDTgMUF5+8DXm9mB8V6NJjZy+PzTXH3G4FPAnOLeG4RGUfUoiYiY1kj8EMzmwp0AssJ3aCb/397d4yCUAwEAXS20mN4Pztr72Fnay14C1sRvIFg6QG+hSls/SAs+F6ZQNIOs4QkuSS5JznPOPeWZF1V+yTXJLvPzWmaHmPEeqiqxVjeJnkmOVbVMu/WbTPjbuCP+EIK4AtVtUpyGg8RAH7K6BMAoCmNGgBAUxo1AICmBDUAgKYENQCApgQ1AICmBDUAgKZeYFs+HpQ41RQAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Gráfico de frequência de Comportamento agressivo = sim:\\n\")\n", - "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='sim'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 423 - }, - "id": "xxyZtxgw2POm", - "outputId": "42edc55a-a03f-4e41-d85e-d19fd008b819" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gráfico de frequência de Comportamento agressivo = não:\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Gráfico de frequência de Comportamento agressivo = não:\\n\")\n", - "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='não'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Uh33_5yC2gG_" - }, - "outputs": [], - "source": [ - "def nuvem_palavras(Agressivo):\n", - " Agressivotexto = ' '.join(texto[texto['Comportamento agressivo?']==Agressivo]['tweet_text_y'])\n", - " wordcloud = WordCloud(\n", - " width = 3000,\n", - " height = 2000,\n", - " background_color = 'black',\n", - " colormap=\"hsv\",\n", - " stopwords = STOPWORDS).generate(str(Agressivotexto))\n", - "\n", - " fig = plt.figure(\n", - " figsize = (8, 4),\n", - " facecolor = 'k',\n", - " edgecolor = 'k',)\n", - " plt.imshow(wordcloud, interpolation = 'bilinear')\n", - " plt.axis('off')\n", - " plt.tight_layout(pad=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 354 - }, - "id": "lC93YnyQ3PhL", - "outputId": "657e4fd7-64c6-4c49-c838-af67503c771b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nuvem de palavras para agressivo sim:\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Nuvem de palavras para agressivo sim:\\n\")\n", - "nuvem_palavras('sim')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 354 - }, - "id": "Y2swNWf13ngt", - "outputId": "4527b640-a194-4f6f-ff77-b62cc41bb160" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nuvem de palavras para agressivo não:\n", - "\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Nuvem de palavras para agressivo não:\\n\")\n", - "nuvem_palavras('não')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "kbd6vgVyuI4Y", - "outputId": "5ccf1807-014e-4509-ef90-3e7bb8ab4a0d" - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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Comportamento agressivo?tweet_authortweet_text_yTamanho
0nãolemathes[lucianohangbr, demorou, rt, lucianohangbr, vi...947
1nãoMaurcio98905595[hospíciolouca, httpstco34bby21hrq, httpstcol9...579
2nãoLunViana[rt, jairbolsonaro, rio, janeiro, rj, govbr, m...1112
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...254
4nãoJoseCar41451194[rt, brazilfight, janaína, paschoal, jamais, b...1130
...............
829nãoCesarNi85939384[rt, claudeluca, alguém, notícia, vão, cassar,...1127
830nãoPauloRo49195361[dindorio, seguindo, patriota, sdv, fechadocom...732
831nãoMarina92011959[betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas...687
832nãoMarcos_28_11_66[rt, drbots2, justiça, condena, influenciador,...1154
833nãoFATIMAC75843178[camelojubeni, konigmachado, marcos281166, kon...958
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834 rows × 4 columns

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\n", - " " - ], - "text/plain": [ - " Comportamento agressivo? tweet_author \\\n", - "0 não lemathes \n", - "1 não Maurcio98905595 \n", - "2 não LunViana \n", - "3 sim felipeleixas \n", - "4 não JoseCar41451194 \n", - ".. ... ... \n", - "829 não CesarNi85939384 \n", - "830 não PauloRo49195361 \n", - "831 não Marina92011959 \n", - "832 não Marcos_28_11_66 \n", - "833 não FATIMAC75843178 \n", - "\n", - " tweet_text_y Tamanho \n", - "0 [lucianohangbr, demorou, rt, lucianohangbr, vi... 947 \n", - "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9... 579 \n", - "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m... 1112 \n", - "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 254 \n", - "4 [rt, brazilfight, janaína, paschoal, jamais, b... 1130 \n", - ".. ... ... \n", - "829 [rt, claudeluca, alguém, notícia, vão, cassar,... 1127 \n", - "830 [dindorio, seguindo, patriota, sdv, fechadocom... 732 \n", - "831 [betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas... 687 \n", - "832 [rt, drbots2, justiça, condena, influenciador,... 1154 \n", - "833 [camelojubeni, konigmachado, marcos281166, kon... 958 \n", - "\n", - "[834 rows x 4 columns]" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", - "texto_preprocessado" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LtWq7dXtLVFA", - "outputId": "7ddddd06-1941-4bc7-9441-4bf52b87615c" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 [lucianohangbr, demorou, rt, lucianohangbr, vi...\n", - "1 [hospíciolouca, httpstco34bby21hrq, httpstcol9...\n", - "2 [rt, jairbolsonaro, rio, janeiro, rj, govbr, m...\n", - "3 [rachelsherazade, vc, chama, jornalismo, vídeo...\n", - "4 [rt, brazilfight, janaína, paschoal, jamais, b...\n", - "Name: tweet_text_y, dtype: object" - ] - }, - "metadata": {}, - "execution_count": 177 - } - ], - "source": [ - "x = texto_preprocessado['tweet_text_y']\n", - "x.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "eXJsyZoo4sVy", - "outputId": "440ecf57-dc4d-4ca1-8769-e3033b44f09a" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0 0\n", - "1 0\n", - "2 0\n", - "3 1\n", - "4 0\n", - "Name: Comportamento agressivo?, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 178 - } - ], - "source": [ - "y = texto_preprocessado['Comportamento agressivo?'].apply(lambda x: 1 if (x == 'sim') else 0)\n", - "y.reset_index(drop=True, inplace=True)\n", - "y.head()" - ] - }, - { - "cell_type": "code", - "source": [ - "vetorizar = CountVectorizer(analyzer=lambda x: x).fit(x)\n", - "x = vetorizar.transform(x)" - ], - "metadata": { - "id": "YeRqmaDVOqx0" - }, - "execution_count": 187, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "print(\"Dimensões da matrix esparsa: \", x.shape)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yxQ3plNqO46w", - "outputId": "f4ee742c-3842-4b9f-afe9-1bcae331c96e" - }, - "execution_count": 188, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Dimensões da matrix esparsa: (834, 102627)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)" - ], - "metadata": { - "id": "_8gkt2hbPTU3" - }, - "execution_count": 202, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "mnb = MultinomialNB()\n", - "\n", - "mnb.fit(x_train,y_train)\n", - "predicao_mnb = mnb.predict(x_test)" - ], - "metadata": { - "id": "JNECf36HPVuX" - }, - "execution_count": 203, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "print(\"Matriz de Confusão - Multinomial Naive Bayes:\\n\")\n", - "print(confusion_matrix(y_test,predicao_mnb))\n", - "print(\"\\nRelatório de Classificação:\",classification_report(y_test,predicao_mnb))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "B-iHxTihQRwi", - "outputId": "c7570435-9171-4a03-a604-7d3ec1439779" - }, - "execution_count": 204, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Matriz de Confusão - Multinomial Naive Bayes:\n", - "\n", - "[[198 14]\n", - " [ 38 1]]\n", - "\n", - "Relatório de Classificação: precision recall f1-score support\n", - "\n", - " 0 0.84 0.93 0.88 212\n", - " 1 0.07 0.03 0.04 39\n", - "\n", - " accuracy 0.79 251\n", - " macro avg 0.45 0.48 0.46 251\n", - "weighted avg 0.72 0.79 0.75 251\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import itertools\n", - "def plot_confusion_matrix(cm, classes,\n", - " normalize=False,\n", - " title='Matriz de Confusão',\n", - " cmap=plt.cm.Blues):\n", - " \"\"\"\n", - " Esta função imprime e plota a matriz de confusão.\n", - " A normalização pode ser aplicada definindo `normalize = True`.\n", - " \"\"\"\n", - " if normalize:\n", - " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", - " print(\"Matriz de confusão normalizada\\n\")\n", - " else:\n", - " print('Matriz de confusão sem normalização\\n')\n", - "\n", - " print(cm)\n", - "\n", - " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", - " plt.title(title)\n", - " plt.colorbar()\n", - " tick_marks = np.arange(len(classes))\n", - " plt.xticks(tick_marks, classes, rotation=45)\n", - " plt.yticks(tick_marks, classes)\n", - "\n", - " fmt = '.2f' if normalize else 'd'\n", - " thresh = cm.max() / 2.\n", - " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", - " plt.text(j, i, format(cm[i, j], fmt),\n", - " horizontalalignment=\"center\",\n", - " color=\"white\" if cm[i, j] > thresh else \"black\")\n", - "\n", - " plt.tight_layout()\n", - " plt.ylabel('True label')\n", - " plt.xlabel('Predicted label')" - ], - "metadata": { - "id": "_LBneh0MQbrz" - }, - "execution_count": 197, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Calculando a confusion matrix\n", - "matriz_confusao = confusion_matrix(y_test, predicao_mnb, labels=[0,1])\n", - "np.set_printoptions(precision=2)\n", - "# Imprimindo a matriz de confusão sem normalização\n", - "plt.figure()\n", - "plot_confusion_matrix(matriz_confusao, classes=['Não=0','Sim=1'],normalize= False, title='Confusion matrix')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 380 - }, - "id": "aWQjHk2-QwGV", - "outputId": "015fd33e-ba99-4d7b-aeac-593c6a264186" - }, - "execution_count": 198, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Matriz de confusão sem normalização\n", - "\n", - "[[198 14]\n", - " [ 38 1]]\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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Classificação do tipo de bot

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "gSt9oJ-Htnqj", - "outputId": "d6e2fa73-e844-4d13-df5c-b3b4cc55e744" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['não se aplica', 'Publicar hashtags', 'Compartilhar links',\n", - " 'publicar hashtags', 'Retweetar', 'compartilhar links', 'Postar',\n", - " 'Responder', 'compartilhar links ', 'Comentar', 'Atacar',\n", - " 'retweetar', 'atacar', 'Publicar imagens ou vídeos',\n", - " 'Mostrar Tweets apagados de atores políticos'], dtype=object)" - ] - }, - "execution_count": 191, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Lista as funções atribuídas ao bots\n", - "funcao_bot = df_handles['Se você fosse atribuir uma função ao bot, qual seria?'].unique()\n", - "funcao_bot" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "collapsed_sections": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file From f9594810a9a7bc1345172eb0e4d3c63be4282bca Mon Sep 17 00:00:00 2001 From: Carla Oliveira Date: Sat, 3 Sep 2022 23:17:39 -0300 Subject: [PATCH 5/9] Criado usando o Colaboratory --- New-Model-From-INCT-DD-Evaluation.ipynb | 10281 +++++++++++++++++----- 1 file changed, 8080 insertions(+), 2201 deletions(-) diff --git a/New-Model-From-INCT-DD-Evaluation.ipynb b/New-Model-From-INCT-DD-Evaluation.ipynb index 4526da2..9d06188 100644 --- a/New-Model-From-INCT-DD-Evaluation.ipynb +++ b/New-Model-From-INCT-DD-Evaluation.ipynb @@ -21,23 +21,23 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pfW-ynZ3vEne", - "outputId": "559ac919-ea54-4767-ece6-e11ca6501b4c" + "outputId": "fe3d9ce9-3c96-4ea7-d6da-f53db36a3d11" }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "[nltk_data] Downloading package rslp to /root/nltk_data...\n", - "[nltk_data] Package rslp is already up-to-date!\n", + "[nltk_data] Unzipping stemmers/rslp.zip.\n", "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n" + "[nltk_data] Unzipping corpora/stopwords.zip.\n" ] } ], @@ -92,72 +92,28 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461 }, "id": "OyGwd_QQvEnh", - "outputId": "a53ff9b4-5bb5-4f5a-a802-8b455b92420e" + "outputId": "792da5c4-24a0-452f-fafd-5cc52adbb5f8" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "1074\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": [ - " Unnamed: 0 error created_at default_profile \\\n", - "0 0 0 2009-06-30 01:05:51+00:00 1.0 \n", - "1 1 0 2019-03-09 11:29:52+00:00 True \n", - "2 2 0 2009-10-20 01:19:19+00:00 False \n", - "3 3 0 2020-05-03 19:06:46+00:00 True \n", - "4 4 0 2021-04-25 20:04:17+00:00 True \n", - "\n", - " description followers_count \\\n", - "0 0 21.0 \n", - "1 0 4192.0 \n", - "2 Feliz é a Nação cujo Deus é o Senhor! #ReageBr... 1341.0 \n", - "3 0 2.0 \n", - "4 0 10.0 \n", - "\n", - " friends_count handle lang location \\\n", - "0 108.0 lemathes 0.0 Brasil, São Paulo \n", - "1 4886.0 Maurcio98905595 0.0 MG , Brasil \n", - "2 1854.0 LunViana 0.0 Araraquara, Brasil \n", - "3 31.0 felipeleixas 0.0 0 \n", - "4 21.0 JoseCar41451194 0.0 0 \n", - "\n", - " name \\\n", - "0 Leandro Mathes \n", - "1 Maurício Lima \n", - "2 Luciana \n", - "3 Felipe \n", - "4 Jose Carlos Marques de Albuquerque \n", - "\n", - " profile_image twitter_id \\\n", - "0 http://pbs.twimg.com/profile_images/1141547105... 5.225325e+07 \n", - "1 http://pbs.twimg.com/profile_images/1104354755... 1.104344e+18 \n", - "2 http://pbs.twimg.com/profile_images/1436716357... 8.373752e+07 \n", - "3 http://pbs.twimg.com/profile_images/1264366970... 1.257024e+18 \n", - "4 http://pbs.twimg.com/profile_images/1429559356... 1.386411e+18 \n", - "\n", - " twitter_is_protected verified withheld_in_countries \n", - "0 0.0 0.0 [] \n", - "1 False False [] \n", - "2 False False [] \n", - "3 False False [] \n", - "4 False False [] " - ], "text/html": [ "\n", - "
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Sim
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Unnamed: 0Unnamed: 0.1tabelaAmostrapÉ Bot?Se você fosse atribuir uma função ao bot, qual seria?Função #2Comportamento agressivo?Comportamento repetitivo com # ou menções?Parece só Retweetar?Só compartilha links?Só faz comentários?Enaltece muito outros usuários?Faz muito uso de emojis?Tem muitos posts sem textos?Unnamed: 14handle
001https://twitter.com/@lemathes0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0lemathes
112https://twitter.com/@Maurcio989055950000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0Maurcio98905595
223https://twitter.com/@LunViana0000.csvnãonão se aplica0nãonãonãonãonãonãonãonão0LunViana
334https://twitter.com/@felipeleixas0000.csvsimPublicar hashtagsAtacarsimsimnãonãonãonãonãonão0felipeleixas
445https://twitter.com/@JoseCar414511940000.csvNãonão se aplica0nãonãonãonãonãonãonãonão0JoseCar41451194
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Só faz comentários? \\\n", + "0 não não não \n", + "1 não não não \n", + "2 não não não \n", + "3 não não não \n", + "4 não não não \n", + "\n", + " Enaltece muito outros usuários? Faz muito uso de emojis? \\\n", + "0 não não \n", + "1 não não \n", + "2 não não \n", + "3 não não \n", + "4 não não \n", + "\n", + " Tem muitos posts sem textos? Unnamed: 14 handle \n", + "0 não 0 lemathes \n", + "1 não 0 Maurcio98905595 \n", + "2 não 0 LunViana \n", + "3 não 0 felipeleixas \n", + "4 não 0 JoseCar41451194 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Lê os dados da aplicação do botometer\n", + "#Busca os dados dos usuários avaliados\n", + "datafile_botometer = \"/content/sample_data/handles_inct.csv\"\n", + "df_botometer = pd.read_csv(datafile_botometer, header = 0)\n", + "#Preenche os valores NaN con 0 apenas para avaliação geral\n", + "df_botometer = df_botometer.fillna(0)\n", + "print(len(df_botometer))\n", + "df_botometer.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "id": "dREze2TlvEn9", + "outputId": "038a129e-2839-4009-e09d-062f956dedd5" + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Avalia os resultados do botometer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'não'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Não'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sim'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_botometer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'É Bot?'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Sim'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'analise_botometer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" + \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3456\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3460\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3361\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3365\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'analise_botometer'" + ] + } + ], + "source": [ + "#Avalia os resultados do botometer\n", + "a = len(df_botometer['analise_botometer'])\n", + "b = len(df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer'])\n", + "c = len(df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer'])\n", + "print(\" \" + str(a) + \" = \" + str(b) + \" + \" + str(c))\n", + "botometer_geral = df_botometer['analise_botometer']\n", + "botometer_nao = df_botometer[(df_botometer['É Bot?'] == 'não') | (df_botometer['É Bot?'] == 'Não')]['analise_botometer']\n", + "botometer_sim = df_botometer[(df_botometer['É Bot?'] == 'sim') | (df_botometer['É Bot?'] == 'Sim')]['analise_botometer']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253 + }, + "id": "DzmZgqDkvEn9", + "outputId": "e8e8ddbf-28de-427e-a18d-314c97c0a804" + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "ignored", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#(11, 6)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbplots\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbotometer_geral\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize =(20, 10)) #(11, 6)\n", "bplots = plt.boxplot([botometer_geral/5, botometer_nao/5, botometer_sim/5, res_geral, res_nao, res_sim], vert = 1, patch_artist = False)\n", @@ -12402,31 +18139,27 @@ }, { "cell_type": "markdown", - "source": [ - "

Análise de Sentimento

" - ], "metadata": { "id": "_XSWWa_1lwQm" - } + }, + "source": [ + "

Análise de Sentimento

" + ] }, { "cell_type": "code", - "source": [ - "!pip install nltk\n", - "!pip install wordcloud" - ], + "execution_count": null, "metadata": { - "id": "Djeb7PUI77DC", - "outputId": "249f99d9-0620-4961-f224-ec93cf564d8b", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "Djeb7PUI77DC", + "outputId": "249f99d9-0620-4961-f224-ec93cf564d8b" }, - "execution_count": 261, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n", @@ -12447,10 +18180,32 @@ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->wordcloud) (1.15.0)\n" ] } + ], + "source": [ + "!pip install nltk\n", + "!pip install wordcloud" ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "joxis7kss1II", + "outputId": "f4abf21f-d2a0-472b-e628-a3098cd93de0" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -12472,42 +18227,20 @@ "from sklearn.metrics import classification_report\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score" - ], - "metadata": { - "id": "joxis7kss1II", - "outputId": "48106ef3-1340-4b79-aa71-80033a60b55f", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 262, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", - "[nltk_data] Package stopwords is already up-to-date!\n" - ] - } ] }, { "cell_type": "code", - "source": [ - "df_handles['Comportamento agressivo?'].head()" - ], + "execution_count": null, "metadata": { - "id": "LHgK1OAttT08", - "outputId": "655728aa-fc89-434f-9550-e4d2afc01d3e", "colab": { "base_uri": "https://localhost:8080/" - } + }, + "id": "LHgK1OAttT08", + "outputId": "4c1edee4-d3e6-4a14-c843-e67601cacdc3" }, - "execution_count": 263, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0 não\n", @@ -12518,67 +18251,39 @@ "Name: Comportamento agressivo?, dtype: object" ] }, + "execution_count": 145, "metadata": {}, - "execution_count": 263 + "output_type": "execute_result" } + ], + "source": [ + "df_handles['Comportamento agressivo?'].head()" ] }, { "cell_type": "code", - "source": [ - "#Seleção do texto e com o rótulo é agressivo ou não\n", - "df_result_merge_text = pd.merge(df_handles, df_users, on=['handle'])\n", - "df_result_merge_text = pd.merge(df_result_merge,df_result_text, left_on=['handle'], right_on=['tweet_author'])\n", - "print(len(df_result_merge_text))\n", - "df_result_merge_text.head(1)" - ], + "execution_count": null, "metadata": { - "id": "UixBJa39kLDg", - "outputId": "c35ab42b-7256-4203-ded0-942c8d293909", "colab": { "base_uri": "https://localhost:8080/", "height": 365 - } + }, + "id": "UixBJa39kLDg", + "outputId": "225d4057-63aa-4ee6-fd69-daf5f84d961b" }, - "execution_count": 264, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "834\n" ] }, { - "output_type": "execute_result", "data": { - "text/plain": [ - " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra p É Bot? \\\n", - "0 0 1 https://twitter.com/@lemathes 0000.csv não \n", - "\n", - " Se você fosse atribuir uma função ao bot, qual seria? Função #2 \\\n", - "0 não se aplica NaN \n", - "\n", - " Comportamento agressivo? Comportamento repetitivo com # ou menções? \\\n", - "0 não não \n", - "\n", - " Parece só Retweetar? ... lang location name \\\n", - "0 não ... 0.0 Brasil, São Paulo Leandro Mathes \n", - "\n", - " profile_image twitter_id \\\n", - "0 http://pbs.twimg.com/profile_images/1141547105... 52253248.0 \n", - "\n", - " twitter_is_protected verified withheld_in_countries tweet_author \\\n", - "0 0.0 0.0 [] lemathes \n", - "\n", - " tweet_text \n", - "0 @LucianoHangBr Já demorou muito!, RT @LucianoH... \n", - "\n", - "[1 rows x 36 columns]" - ], "text/html": [ "\n", - "
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