diff --git "a/Apresenta\303\247\303\243o-Grupo3A.pdf" "b/Apresenta\303\247\303\243o-Grupo3A.pdf" new file mode 100644 index 0000000..0fe78f1 Binary files /dev/null and "b/Apresenta\303\247\303\243o-Grupo3A.pdf" differ diff --git a/New-Model-From-INCT-DD-Evaluation.ipynb b/New-Model-From-INCT-DD-Evaluation.ipynb index 54ea138..652a293 100644 --- a/New-Model-From-INCT-DD-Evaluation.ipynb +++ b/New-Model-From-INCT-DD-Evaluation.ipynb @@ -1,1440 +1,20482 @@ { - "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": 361, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pfW-ynZ3vEne", + "outputId": "44f3f7a3-06f7-46f3-cb20-82e538461821" + }, + "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", + "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": 362, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461 + }, + "id": "OyGwd_QQvEnh", + "outputId": "a48191b3-938e-454b-cd99-ba7681dcb7f7" + }, + "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! #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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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[]
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 362 + } + ], + "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": 363, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UjegnTdGvEnj", + "outputId": "bd7a1988-7af2-42cc-b500-6d480c93b2fc" + }, + "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": 363 + } + ], + "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": "code", + "execution_count": 364, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 478 + }, + "id": "K-wiZuVNaCTz", + "outputId": "d9d29d0c-08b4-4e65-ccd0-09b90cfd0951" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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 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? 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 NaN lemathes \n", + "1 não NaN Maurcio98905595 \n", + "2 não NaN LunViana \n", + "3 não NaN felipeleixas \n", + "4 não NaN JoseCar41451194 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 364 + } + ], + "source": [ + "df_handles.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": 365, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 249 + }, + "id": "YH3gaVLHvEnl", + "outputId": "e34ad907-7fdc-4c2c-dfe3-3a5d6efc3cac" + }, + "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", + "\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! " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 365 + } + ], + "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": 366, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MxTnMw6evEnm", + "outputId": "19281199-b56f-4a65-971f-5c82aac55ece" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['0.0', 'False', 'True', False, True], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 366 + } + ], + "source": [ + "#identifica os formatos existentes\n", + "df_timeline['tweet_is_retweet'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6kAf3fAFvEnn", + "outputId": "a112b693-d287-476f-f86c-d51f00c24d6f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['não', 'sim'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 367 + } + ], + "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": 368, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KRPYHx4-vEnn", + "outputId": "53aacd65-20a9-41c2-a934-05444a6e7d75" + }, + "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": 368 + } + ], + "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": 369, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 310 + }, + "id": "L7lkmE_yvEno", + "outputId": "eb8aaa31-b926-44b2-e76f-da93085e1789" + }, + "outputs": [ + { + "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", + "\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", + "\n", + " tweet_hashtags ... tweet_id_str tweet_is_retweet tweet_lang \\\n", + "0 [] ... 1501379987747876874 0.0 pt \n", + "1 [] ... 1501379796210757632 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", + "\n", + " tweet_text retweet_tratado \\\n", + "0 @LucianoHangBr Já demorou muito! não \n", + "1 RT @LucianoHangBr: A vida precisa continuar e ... não \n", + "\n", + " tweet_com_rt_tratado retweet_e_tweet_com_rt_tratado \n", + "0 não não \n", + "1 sim sim \n", + "\n", + "[2 rows x 21 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "

2 rows × 21 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 369 + } + ], + "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)\n", + "df_timeline.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 939 + }, + "id": "C_YXOwhCvEnp", + "outputId": "ad0f5ea1-8408-480e-f403-a378ff84baf0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0 error tweet_author tweet_author_id_str \\\n", + "1 1 NaN lemathes 52253248 \n", + "5 5 NaN lemathes 52253248 \n", + "6 6 NaN lemathes 52253248 \n", + "7 7 NaN lemathes 52253248 \n", + "8 8 NaN lemathes 52253248 \n", + "... ... ... ... ... \n", + "82406 82406 NaN FATIMAC75843178 1349784643244093440 \n", + "82408 82408 NaN FATIMAC75843178 1349784643244093440 \n", + "82409 82409 NaN FATIMAC75843178 1349784643244093440 \n", + "82410 82410 NaN FATIMAC75843178 1349784643244093440 \n", + "82411 82411 NaN FATIMAC75843178 1349784643244093440 \n", + "\n", + " tweet_contributors tweet_created_at tweet_favorite_count \\\n", + "1 NaN 2022-03-09 02:10:12+00:00 0.0 \n", + "5 NaN 2022-02-27 13:38:14+00:00 0.0 \n", + "6 NaN 2022-02-18 04:17:53+00:00 0.0 \n", + "7 NaN 2022-02-18 04:11:31+00:00 0.0 \n", + "8 NaN 2022-02-18 04:10:00+00:00 0.0 \n", + "... ... ... ... \n", + "82406 NaN 2022-03-17 12:10:29+00:00 0.0 \n", + "82408 NaN 2022-03-17 12:09:52+00:00 0.0 \n", + "82409 NaN 2022-03-17 12:09:18+00:00 0.0 \n", + "82410 NaN 2022-03-17 12:08:46+00:00 0.0 \n", + "82411 NaN 2022-03-17 12:08:38+00:00 0.0 \n", + "\n", + " tweet_favorited tweet_geo tweet_hashtags ... tweet_id_str \\\n", + "1 False NaN [] ... 1501379796210757632 \n", + "5 False NaN [] ... 1497929065302482946 \n", + "6 False NaN [] ... 1494526561902546944 \n", + "7 False NaN [] ... 1494524957593845762 \n", + "8 False NaN [] ... 1494524573919940609 \n", + "... ... ... ... ... ... \n", + "82406 False NaN [] ... 1504429966729138176 \n", + "82408 False NaN [] ... 1504429810352898052 \n", + "82409 False NaN [] ... 1504429669613031426 \n", + "82410 False NaN [] ... 1504429535818924033 \n", + "82411 False NaN [] ... 1504429502369259522 \n", + "\n", + " tweet_is_retweet tweet_lang tweet_place tweet_retweeted \\\n", + "1 False pt NaN False \n", + "5 False pt NaN False \n", + "6 False pt NaN False \n", + "7 False pt NaN False \n", + "8 False pt NaN False \n", + "... ... ... ... ... \n", + "82406 False pt NaN False \n", + "82408 False pt NaN False \n", + "82409 False pt NaN False \n", + "82410 False pt NaN False \n", + "82411 False pt NaN False \n", + "\n", + " tweet_source tweet_text \\\n", + "1 Twitter for Android RT @LucianoHangBr: A vida precisa continuar e ... \n", + "5 Twitter for Android RT @roxmo: Puxa, que pena, passou tão perto!… ... \n", + "6 Twitter for Android RT @mila_sayuri: Alguém poderia confirmar se e... \n", + "7 Twitter for Android RT @RenzoGracieBJJ: Quando postei aqui o vídeo... \n", + "8 Twitter for Android RT @roxmo: Vc confia nas urnas eletrônicas? \n", + "... ... ... \n", + "82406 Twitter for Android RT @EdmarVencedor: @BelaBonoro @OsvaldoLimaJni... \n", + "82408 Twitter for Android RT @BelaBonoro: @OsvaldoLimaJni1 @CeliaSLeao1 ... \n", + "82409 Twitter for Android RT @carlosjordy: Ciro sincero diz de quem é a ... \n", + "82410 Twitter for Android RT @CarlaZambelli38: ATENÇÃO ao alerta do Pres... \n", + "82411 Twitter for Android RT @CarlaZambelli38: Vai vendo...\\n\\nO ex-pres... \n", + "\n", + " retweet_tratado tweet_com_rt_tratado retweet_e_tweet_com_rt_tratado \n", + "1 não sim sim \n", + "5 não sim sim \n", + "6 não sim sim \n", + "7 não sim sim \n", + "8 não sim sim \n", + "... ... ... ... \n", + "82406 não sim sim \n", + "82408 não sim sim \n", + "82409 não sim sim \n", + "82410 não sim sim \n", + "82411 não sim sim \n", + "\n", + "[52130 rows x 21 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
..................................................................
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
8241082410NaNFATIMAC758431781349784643244093440NaN2022-03-17 12:08:46+00:000.0FalseNaN[]...1504429535818924033FalseptNaNFalseTwitter for AndroidRT @CarlaZambelli38: ATENÇÃO ao alerta do Pres...nãosimsim
8241182411NaNFATIMAC758431781349784643244093440NaN2022-03-17 12:08:38+00:000.0FalseNaN[]...1504429502369259522FalseptNaNFalseTwitter for AndroidRT @CarlaZambelli38: Vai vendo...\\n\\nO ex-pres...nãosimsim
\n", + "

52130 rows × 21 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 370 + } + ], + "source": [ + "df_timeline[df_timeline[\"retweet_e_tweet_com_rt_tratado\"] == 'sim']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gQ4ZjbN5vEnp" + }, + "source": [ + "Extrai a diferença em segundos entre as postagens do usuário" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q8UEtNgzvEnp", + "outputId": "69584116-c0c7-4bf3-d1a6-2a1608466b10" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "lemathes - 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" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "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" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Valdir_25 - 16 - 10217\n", + "\n", + "HermesMachadoAP - 26 - 3416\n", + "\n", + "euclaudemir - 4 - 8076\n", + "\n", + "LongoMarlongo - 5 - 117\n", + "\n", + "Tadeu88537223 - 4 - 147\n", + "\n", + "EliasBispodeCe1 - 3 - 63\n", + "\n", + "lessa_tadeu - 100000 - 1000\n", + "\n", + "kentyan71 - 4 - 136\n", + "\n", + "Sirenite1 - 1 - 142\n", + "\n", + "elechimamil - 6 - 423\n", + "\n", + "mhelena17 - 4 - 74\n", + "\n", + "victorcalazans - 15 - 405\n", + "\n", + "AnaPedros2308 - 4 - 136\n", + "\n", + "ricardo_lacava - 24 - 235\n", + "\n", + "AslanDeHogwarts - 10 - 1933\n", + "\n", + "Patriota7M - 0 - 24\n", + "\n", + "luGusmao1 - 2 - 15\n", + "\n", + "GilAndrade5 - 6 - 66\n", + "\n", + "ClaytonSampaio5 - 0 - 20900\n", + "\n", + "filhadefridak - 17 - 14945\n", + "\n", + "lucas_neves164 - 100000 - 1000\n", + "\n", + "Thiago48035185 - 6 - 71\n", + "\n", + "herciconti - 0 - 110\n", + "\n", + "FlaviaPauletti - 13 - 2250\n", + "\n", + "ElielAmorim9 - 14 - 207\n", + "\n", + "Lunar_Vante - 6 - 380\n", + "\n", + "lemosmarl - 3 - 10776\n", + "\n", + "joaocarlosjc - 5 - 1533\n", + "\n", + "georgedebarros - 16 - 314\n", + "\n", + "valterpn - 2 - 290\n", + "\n", + "Mariade25585540 - 5 - 249\n", + "\n", + "NTresolavy - 6 - 193\n", + "\n", + "jonny_1309 - 3 - 27605\n", + "\n", + "Dani_BernMor - 16 - 652\n", + "\n", + "lui45807682 - 2 - 180\n", + "\n", + "OluasSnanaj - 22 - 2964\n", + "\n", + "abreumartha - 7 - 104\n", + "\n", + "deuzemaroliveir - 5 - 49\n", + "\n", + "Tahuamello - 100000 - 1000\n", + "\n", + "BorjaoOp - 100000 - 1000\n", + "\n", + "MelchiranL - 3 - 4607\n", + "\n", + "_mcalheiros - 2 - 65\n", + "\n", + "WalterMessias - 5 - 21402\n", + "\n", + "Gaby_Rodriguess - 2 - 148\n", + "\n", + "Jorge48591816 - 15 - 208\n", + "\n", + "TniaBitencourt1 - 3 - 204\n", + "\n", + "DieGodArau - 14 - 18540\n", + "\n", + "Raylan007 - 1072 - 34596.0\n", + "\n", + "NegoBoy73 - 4 - 3361\n", + "\n", + "EsquerdopataDe - 11 - 6955\n", + "\n", + "paulasimonii - 8 - 523\n", + "\n", + "AlvesEvellynn - 23 - 4242\n", + "\n", + "AnaPaulaMay5 - 3 - 2328\n", + "\n", + "DestraDireita - 27 - 6982\n", + "\n", + "tonisepon - 55 - 1745\n", + "\n", + "PaulaLi01322083 - 100000 - 1000\n", + "\n", + "calbernardo_ - 7 - 18837\n", + "\n", + "GenivalDamasce1 - 2 - 230\n", + "\n", + "magg_sarah - 5 - 320\n", + "\n", + "LOBO24468415 - 100000 - 1000\n", + "\n", + "crivelenda - 100000 - 1000\n", + "\n", + "JottaFilho1 - 100000 - 1000\n", + "\n", + "Verinhassp - 0 - 77\n", + "\n", + "abieloliveira - 11 - 4160\n", + "\n", + "EmNavegacao - 11 - 5543\n", + "\n", + "NardezAndrea - 36 - 2369\n", + "\n", + "Regis_lops - 15 - 151\n", + "\n", + "lutricolor_ - 100000 - 1000\n", + "\n", + "Simonejjp - 100000 - 1000\n", + "\n", + "lidiacarvalhol1 - 7 - 159\n", + "\n", + "WagnerBicas - 28 - 3519\n", + "\n", + "DidoneCleiton - 100000 - 1000\n", + "\n", + "silmaracrovador - 100000 - 1000\n", + "\n", + "RicardodeFigue7 - 4 - 152\n", + "\n", + "whatsyourtalle - 0 - 2125\n", + "\n", + "pietrolovsky - 1 - 218\n", + "\n", + "LueltonB - 17 - 1023\n", + "\n", + "twtDireita - 100000 - 1000\n", + "\n", + "CaracasFabiano - 100000 - 1000\n", + "\n", + "edicabral58a1 - 90 - 25409\n", + "\n", + "_vininhs - 100000 - 1000\n", + "\n", + "KoenigEdson - 3 - 62\n", + "\n", + "soniabubeck - 21 - 4705\n", + "\n", + "quiteria2011 - 34 - 19295\n", + "\n", + "AugustoRdePaiva - 2 - 58\n", + "\n", + "Gi_fsantos - 4 - 155\n", + "\n", + "luisfreis - 6 - 44\n", + "\n", + "PedroPo20536053 - 11 - 119\n", + "\n", + "aluisionvj - 3 - 23625\n", + "\n", + "ealves74 - 13 - 6651\n", + "\n", + "belabr30 - 19 - 291\n", + "\n", + "ItamarTeixeir15 - 0 - 5096\n", + "\n", + "srmukznc - 1 - 21953\n", + "\n", + "Osenildo9 - 9 - 18012\n", + "\n", + "ederfuzzo - 4 - 61208.0\n", + "\n", + "EdvaldoPires6 - 100000 - 1000\n", + "\n", + "PauloBarros57 - 0 - 7149\n", + "\n", + "KELLICSANT - 7 - 27516\n", + "\n", + "anakatiarodrig3 - 3 - 169\n", + "\n", + "machadorenato - 18 - 42580\n", + "\n", + "GelidaMara - 100000 - 1000\n", + "\n", + "Viceh_ - 1 - 349\n", + "\n", + "BorgesGf - 6 - 5355\n", + "\n", + "eduoliveirawd - 100000 - 1000\n", + "\n", + "Ronilsondejesu7 - 3 - 87\n", + "\n", + "Oli00657400 - 8 - 83\n", + "\n", + "NorisGa17696072 - 13 - 8736\n", + "\n", + "AguiarRedson - 25 - 32319\n", + "\n", + "NiviaMoura11 - 32 - 12850.0\n", + "\n", + "laurkimj - 100000 - 1000\n", + "\n", + "FabianYamazato - 10 - 25311\n", + "\n", + "keniaxavier16 - 13 - 11656\n", + "\n", + "jaugcord - 0 - 128\n", + "\n", + "SandraC30214411 - 6 - 458\n", + "\n", + "MariaAu13872788 - 16 - 1415\n", + "\n", + "julianolemos87 - 5 - 15758\n", + "\n", + "MoaSilva53 - 19 - 430\n", + "\n", + "UmbigodaBanana - 12 - 3826\n", + "\n", + "pereirajsp - 7 - 106\n", + "\n", + "Chan_adv - 100000 - 1000\n", + "\n", + "AntiAnticomuna - 53 - 17047\n", + "\n", + "Rose80369683 - 52 - 25432\n", + "\n", + "Cesar58Garcia - 28 - 15711\n", + "\n", + "juliocelso5 - 11 - 5521\n", + "\n", + "AmbrozioSolange - 5 - 355\n", + "\n", + "WillianRamosFca - 3 - 97\n", + "\n", + "adrianefonsec10 - 10 - 4243\n", + "\n", + "Gaigaia - 11 - 17941\n", + "\n", + "JlViterbo - 21 - 41781\n", + "\n", + "jdls1966 - 14 - 13532\n", + "\n", + "AnaLuci40400673 - 3 - 31\n", + "\n", + "Reginal35976929 - 25 - 30399\n", + "\n", + "NicolettoCleidi - 14 - 24082\n", + "\n", + "Uanderson1980 - 23 - 19538\n", + "\n", + "CassiaContarin - 8 - 10564\n", + "\n", + "operacaocondor - 91 - 38429\n", + "\n", + "William01373135 - 49 - 32877\n", + "\n", + "iranidelucena - 6 - 1190\n", + "\n", + "Vascosfv - 20 - 15282\n", + "\n", + "AndreaFerrarezi - 9 - 28\n", + "\n", + "SixelMalvado - 100000 - 1000\n", + "\n", + "MorgaoCidinha - 11 - 74\n", + "\n", + "sergiosn1977 - 22 - 17471\n", + "\n", + "LeisaDrumond - 11 - 1243\n", + "\n", + "FbioDan98630923 - 37 - 34445\n", + "\n", + "Fo_inca - 5 - 20490\n", + "\n", + "jcesardlima - 18 - 22610\n", + "\n", + "RafaeLuann1 - 19 - 12493\n", + "\n", + "MaisadeAndrade2 - 40 - 41017\n", + "\n", + "OdairMontiel - 100000 - 1000\n", + "\n", + "marcinhacunh - 0 - 240\n", + "\n", + "Neuilly39868830 - 10 - 35553\n", + "\n", + "TeixeiraAldicea - 0 - 10959\n", + "\n", + "GilmarSchmitz5 - 21 - 10784\n", + "\n", + "AlexSiq94391416 - 11 - 14377\n", + "\n", + "yang_rafa_ - 100000 - 1000\n", + "\n", + "pedrwgotwo - 100000 - 1000\n", + "\n", + "CunhaRibelro - 8 - 19483\n", + "\n", + "AnaMarc55528861 - 16 - 28868\n", + "\n", + "RafaelVeeck - 5 - 37247\n", + "\n", + "edu88366089 - 2 - 18348\n", + "\n", + "poy01 - 28 - 8465\n", + "\n", + "elennnm - 12 - 1119\n", + "\n", + "Deiviss76927679 - 9 - 25422\n", + "\n", + "Reinald0_azedo - 33 - 607\n", + "\n", + "paiva1982 - 17 - 31766\n", + "\n", + "RosarioCampelo - 18 - 823\n", + "\n", + "benwepozi - 6 - 2080\n", + "\n", + "waldema93547310 - 61 - 41275\n", + "\n", + "edsonbjl - 3 - 31\n", + "\n", + "RomagnaniLite - 2 - 386\n", + "\n", + "DeniseLuz3 - 7 - 11840\n", + "\n", + "VagnerMouraCam1 - 42 - 2665\n", + "\n", + "leandrofmax - 10 - 6996\n", + "\n", + "jjggukie__ - 100000 - 1000\n", + "\n", + "kalaumusico - 22 - 623\n", + "\n", + "g_della_ - 2 - 2289\n", + "\n", + "DILMAFREITAS333 - 12 - 12921\n", + "\n", + "AragaoJosafa - 16 - 35791\n", + "\n", + "Tony18822268 - 35 - 26150\n", + "\n", + "MartoniTony - 100000 - 1000\n", + "\n", + "TniaMar90422263 - 44 - 32955\n", + "\n", + "crbastelli - 16 - 131\n", + "\n", + "priscil16465789 - 0 - 15315\n", + "\n", + "PAULAAL37165233 - 100000 - 1000\n", + "\n", + "Ronerio9 - 100000 - 1000\n", + "\n", + "Paulokastro70 - 24 - 44903.0\n", + "\n", + "ana99157293 - 77 - 41263\n", + "\n", + "Marciocarlosro1 - 535 - 38125.5\n", + "\n", + "kadddyy - 24 - 5014\n", + "\n", + "garotadotobio - 100000 - 1000\n", + "\n", + "natalfut - 3 - 49\n", + "\n", + "charleslarubia - 5 - 38\n", + "\n", + "gustavolondon - 3 - 469\n", + "\n", + "AlexandrRangel - 30 - 6664\n", + "\n", + "DanielR12085425 - 16 - 216\n", + "\n", + "a651b520d1434a6 - 21 - 16090\n", + "\n", + "BassitLilian - 2 - 45\n", + "\n", + "kittynett - 100000 - 1000\n", + "\n", + "marcelosalest - 6 - 49\n", + "\n", + "Jackiebcd - 20 - 18621\n", + "\n", + "emilinha_f - 100000 - 1000\n", + "\n", + "Michele17717875 - 2 - 116\n", + "\n", + "LuisLima098 - 4 - 71\n", + "\n", + "Eliomar31491806 - 57 - 22388\n", + "\n", + "DouradoROBERT1 - 8 - 12110\n", + "\n", + "adrianoluiz1909 - 2 - 53\n", + "\n", + "weltimao - 6 - 96\n", + "\n", + "MarcosR41279094 - 6 - 201\n", + "\n", + "JeronimoOnofre - 2 - 9041\n", + "\n", + "MauroCavalli2 - 11 - 50\n", + "\n", + "nicole2776 - 2 - 58\n", + "\n", + "tisemherois - 0 - 176\n", + "\n", + "lopesandre36 - 12 - 6600\n", + "\n", + "urukihai1797 - 1 - 8\n", + "\n", + "MarciaMBACruz - 30 - 21516\n", + "\n", + "RosiMacedo11 - 21 - 14637\n", + "\n", + "13valber1 - 3 - 1651\n", + "\n", + "WagnerL83 - 3 - 112\n", + "\n", + "MallonTania - 19 - 7000\n", + "\n", + "RLCoutinho2 - 5 - 43\n", + "\n", + "Marrockys - 5 - 53\n", + "\n", + "CesarRi95836529 - 23 - 29049\n", + "\n", + "Aldeniza_coffee - 100000 - 1000\n", + "\n", + "VandsoneyC - 13 - 11664\n", + "\n", + "Glendersongomes - 7 - 362\n", + "\n", + "ferrazmarcos - 3 - 25361\n", + "\n", + "lvfmtav - 3 - 146\n", + "\n", + "ACamargo241 - 26 - 4207\n", + "\n", + "Valrosa_R - 7 - 1511\n", + "\n", + "CATU_CAMILA13 - 100000 - 1000\n", + "\n", + "stefanspipe1 - 100000 - 1000\n", + "\n", + "GideaoPereira38 - 11 - 15607\n", + "\n", + "roserezendee - 10 - 2307\n", + "\n", + "liagraca - 2 - 143\n", + "\n", + "lucianaleandrol - 3 - 23\n", + "\n", + "Danilacivita1 - 100000 - 1000\n", + "\n", + "Claudia_NF - 5 - 7255\n", + "\n", + "JoaoPauloSoto55 - 8 - 234\n", + "\n", + "fullenbach - 1 - 33\n", + "\n", + "OdaRoseli - 7 - 1196\n", + "\n", + "JuneilsonC - 5 - 44\n", + "\n", + "JoseleneSantia1 - 27 - 87\n", + "\n", + "goianopesca - 1 - 15\n", + "\n", + "Maximo48154518 - 5 - 123\n", + "\n", + "maffei_dos - 4 - 56\n", + "\n", + "marciaafranca - 0 - 159\n", + "\n", + "matthaews - 100000 - 1000\n", + "\n", + "IzabelaCSO - 2 - 640\n", + "\n", + "Outsiderbrasuca - 15 - 632\n", + "\n", + "SilvioR66707206 - 0 - 112\n", + "\n", + "TarcesP - 8 - 130\n", + "\n", + "Mar_quex - 4 - 63\n", + "\n", + "silves_marcos - 3 - 45\n", + "\n", + "clabj4ever - 10 - 198\n", + "\n", + "willians_mello - 2 - 42\n", + "\n", + "LMMAGLIANO - 3 - 2201\n", + "\n", + "MCJohn61699095 - 20 - 314\n", + "\n", + "ViiRodrigues15 - 100000 - 1000\n", + "\n", + "WHOXVOM - 8 - 2556\n", + "\n", + "pedro_bhz - 5 - 636\n", + "\n", + "joseant34635348 - 4 - 7242\n", + "\n", + "ElisCarmem - 1 - 16\n", + "\n", + "nation_som - 7 - 48\n", + "\n", + "bolsona65717680 - 3 - 147\n", + "\n", + "DanielLozov - 3 - 11849\n", + "\n", + "Carcliocarclio - 2 - 106\n", + "\n", + "BoneteMari - 100000 - 1000\n", + "\n", + "fabiojmolina - 2 - 13\n", + "\n", + "cramos72 - 6 - 1782\n", + "\n", + "samuelggomes - 3 - 183\n", + "\n", + "RogerioMR1970 - 9 - 185\n", + "\n", + "RudimarCouto - 6 - 736\n", + "\n", + "fhau12 - 3 - 25\n", + "\n", + "hgyn51 - 10 - 168\n", + "\n", + "carlosfur77 - 24 - 860\n", + "\n", + "hopenyourgalaxy - 3 - 47\n", + "\n", + "Dimas70476533 - 2 - 128\n", + "\n", + "sussouza - 3 - 1010\n", + "\n", + "ligiagambol - 4 - 290\n", + "\n", + "bloqueadoalmn - 8 - 4762\n", + "\n", + "andymarianoo - 5 - 259\n", + "\n", + "RomaoAri - 4 - 169\n", + "\n", + "MoLene12 - 8 - 258\n", + "\n", + "marcusPITTERnew - 4 - 75\n", + "\n", + "ThaiSinhaAaAaa - 10 - 367\n", + "\n", + "neydoflamengo - 6 - 162\n", + "\n", + "valquiriamaria1 - 11 - 38394\n", + "\n", + "rosesouzac1 - 4 - 16705\n", + "\n", + "MarcosA54890714 - 100000 - 1000\n", + "\n", + "Fabio43402210 - 2 - 110\n", + "\n", + "FlvioFeijo - 25 - 152\n", + "\n", + "juniojack97 - 6 - 10996\n", + "\n", + "Guerreiro7621 - 6 - 46\n", + "\n", + "barbosa336 - 6 - 11828\n", + "\n", + "Jnior88682564 - 13 - 25715\n", + "\n", + "alemdomarx_ - 100000 - 1000\n", + "\n", + "recoleonatural - 6 - 6843\n", + "\n", + "AJ_Grima - 100000 - 1000\n", + "\n", + "JuniorPessoa_ - 11 - 27892\n", + "\n", + "Celio30065920 - 19 - 25377\n", + "\n", + "Heloisa__Campos - 2 - 3754\n", + "\n", + "Nely40337415 - 15 - 29846\n", + "\n", + "VilmaFerreiraO3 - 18 - 15143\n", + "\n", + "SKlinnt - 9 - 25115\n", + "\n", + "100_bolsonaro - 11 - 131\n", + "\n", + "thiagotortelote - 6 - 2880\n", + "\n", + "GuerreiroPatri8 - 4 - 94\n", + "\n", + "MichelKrieck - 18 - 7062\n", + "\n", + "Juliano13110 - 117 - 14226\n", + "\n", + "lvswood - 1 - 5\n", + "\n", + "ValdethQueiroz - 1 - 8207\n", + "\n", + "Eckerth - 75 - 11435\n", + "\n", + "AngelitaSantos_ - 10 - 269\n", + "\n", + "Melkpsobrinho - 3 - 10\n", + "\n", + "MayconCarlosSa1 - 18 - 23424\n", + "\n", + "JOSSANT30557087 - 4 - 51\n", + "\n", + "reinacyjr - 13 - 1310\n", + "\n", + "luaanmagnata - 6 - 4355\n", + "\n", + "elzafranzon - 2 - 298\n", + "\n", + "VinaoAlvino - 12 - 6061\n", + "\n", + "AnaCris91315627 - 6 - 157\n", + "\n", + "OZINHO1 - 12 - 158\n", + "\n", + "nandoguedex - 6 - 1207\n", + "\n", + "PAULOROBERTO287 - 21 - 159\n", + "\n", + "chrisbalth - 27 - 6490\n", + "\n", + "Claudio52664728 - 5 - 1386\n", + "\n", + "GueirosTamar - 12 - 13066\n", + "\n", + "monica74658081 - 14 - 2660\n", + "\n", + "HtvRonaldo - 11 - 28383\n", + "\n", + "marceloslone - 1 - 126\n", + "\n", + "HipolitoAugusto - 16 - 6693\n", + "\n", + "marcelinhosala2 - 17 - 29513\n", + "\n", + "CassiNFernandes - 26 - 21904\n", + "\n", + "wal021222 - 2 - 17985\n", + "\n", + "cronicasdumapro - 2 - 21927\n", + "\n", + "Fernand15613612 - 39 - 26131\n", + "\n", + "CRetroz - 12 - 240\n", + "\n", + "Beea__sz24 - 100000 - 1000\n", + "\n", + "MontotoOlga - 11 - 4178\n", + "\n", + "SUPER_ANA_DF - 7 - 48\n", + "\n", + "robsondcarneiro - 28 - 18070\n", + "\n", + "RosileneFreire7 - 18 - 225\n", + "\n", + "RenatoCoelhoFre - 14 - 20913\n", + "\n", + "Juliano50617961 - 3 - 3380\n", + "\n", + "AndrCam70781993 - 7 - 8142\n", + "\n", + "pedro_paravito - 12 - 23974\n", + "\n", + "cyberhannya - 100000 - 1000\n", + "\n", + "FavinhaMarcela - 24 - 564\n", + "\n", + "Jocadeol - 24 - 25227\n", + "\n", + "AControld - 14 - 6127\n", + "\n", + "IverTinny - 100000 - 1000\n", + "\n", + "DarlanSattler - 5 - 234\n", + "\n", + "limallanitta - 100000 - 1000\n", + "\n", + "lombardijc - 13 - 1090\n", + "\n", + "LuizCarlosdeAb7 - 3 - 218\n", + "\n", + "MrciaSueliLope1 - 8 - 28558\n", + "\n", + "SoaresOg - 22 - 5314\n", + "\n", + "DanielOgelio - 27 - 28365\n", + "\n", + "Anubis66150815 - 6 - 291\n", + "\n", + "Liliane73428366 - 30 - 25899\n", + "\n", + "Eduardosobrosa - 5 - 249\n", + "\n", + "BarbosaVoss - 8 - 182\n", + "\n", + "pjiart - 4 - 45\n", + "\n", + "Rondine86725238 - 9 - 22\n", + "\n", + "DireitaSilvio - 100000 - 1000\n", + "\n", + "Marcelo92475572 - 0 - 2710\n", + "\n", + "HumbertoRn70 - 16 - 28770\n", + "\n", + "AnaileRiber - 3 - 412\n", + "\n", + "Kione11674327 - 4 - 1633\n", + "\n", + "kistrose - 16 - 45\n", + "\n", + "AnaPaulaMJones - 3 - 108\n", + "\n", + "MidiSilva14 - 20 - 23384\n", + "\n", + "andrea_nardez - 177 - 22199\n", + "\n", + "overdosejikook - 100000 - 1000\n", + "\n", + "LuciaSa42444213 - 9 - 18999\n", + "\n", + "Valeria84226492 - 28 - 2160\n", + "\n", + "marlons73192679 - 8 - 2564\n", + "\n", + "nayanemss - 40 - 95\n", + "\n", + "varleinunes1 - 5 - 31636\n", + "\n", + "Elizabe95664865 - 18 - 191\n", + "\n", + "Warrior_of_good - 9 - 74\n", + "\n", + "fernand91213693 - 2 - 86\n", + "\n", + "ch4nlover - 100000 - 1000\n", + "\n", + "danisasantos99 - 100000 - 1000\n", + "\n", + "llE0n44d00 - 100000 - 1000\n", + "\n", + "MichelleElvisL1 - 14 - 24320\n", + "\n", + "LuizCar70327315 - 6 - 23316\n", + "\n", + "Deisi71064986 - 8 - 45\n", + "\n", + "DiogoJo93445299 - 4 - 278\n", + "\n", + "JoelLopesLima3 - 25899 - 62623.5\n", + "\n", + "AntonyJojohn - 11 - 216\n", + "\n", + "jcsobralchagas - 5 - 60\n", + "\n", + "DeusaMonte - 100000 - 1000\n", + "\n", + "EdsonTe40594908 - 100000 - 1000\n", + "\n", + "Sete42142987 - 2 - 16\n", + "\n", + "KLINGERMARCOS10 - 100000 - 1000\n", + "\n", + "irenetbalaton - 2 - 20\n", + "\n", + "ServioTul10 - 4 - 79\n", + "\n", + "Leezinh0 - 0 - 1130\n", + "\n", + "MarleneArditti - 3 - 28\n", + "\n", + "PovoPoder - 3 - 70\n", + "\n", + "FernandoBrusto - 2 - 32\n", + "\n", + "Rodrigo71536761 - 100000 - 1000\n", + "\n", + "simone_ss2019 - 7 - 93\n", + "\n", + "josivaldo1955 - 3 - 55\n", + "\n", + "OliveiraMota6 - 57 - 29085\n", + "\n", + "Luci59746961 - 2 - 29\n", + "\n", + "Ricardo32738682 - 6 - 38\n", + "\n", + "heliosimoes65 - 6 - 23\n", + "\n", + "teresinhalopes - 10 - 151\n", + "\n", + "DairoSoares - 3 - 36\n", + "\n", + "m_scarabellii - 1 - 62\n", + "\n", + "Karollamorimm - 1 - 181\n", + "\n", + "EmersonPreto83 - 7 - 5267\n", + "\n", + "CIroJnior2 - 3 - 80\n", + "\n", + "ursogalli - 1 - 126\n", + "\n", + "Elaine16304657 - 6 - 68\n", + "\n", + "Cynthia_Siqq2 - 3 - 21\n", + "\n", + "UlyssesBraga1 - 1 - 13\n", + "\n", + "CELSOPEREIRADO4 - 2 - 22\n", + "\n", + "caldeira310397 - 100000 - 1000\n", + "\n", + "jtalmeida2021 - 100000 - 1000\n", + "\n", + "asinhorinha - 11 - 163\n", + "\n", + "MauraMarts - 3 - 66\n", + "\n", + "CarlaSoaresdeC1 - 4 - 30\n", + "\n", + "claudiarkl - 100000 - 1000\n", + "\n", + "mineirotchotcho - 7 - 156\n", + "\n", + "SilvanaSaunier - 1 - 44\n", + "\n", + "dfarias88 - 1 - 6\n", + "\n", + "FlaviaFrescurat - 4 - 12\n", + "\n", + "SManipulado - 3 - 59\n", + "\n", + "WojownikCiemnos - 100000 - 1000\n", + "\n", + "Vandersonlma - 23 - 543\n", + "\n", + "edkibel - 0 - 9\n", + "\n", + "bacelar_adelson - 4 - 21\n", + "\n", + "HDNETO25 - 5 - 26\n", + "\n", + "Dranasm - 3 - 27\n", + "\n", + "leilavitoreli - 5 - 24\n", + "\n", + "NaoSouO97825936 - 100000 - 1000\n", + "\n", + "RodrigoF70 - 2 - 13\n", + "\n", + "MEGAZORDEOPRES1 - 7 - 40\n", + "\n", + "AndressaTomaze5 - 5 - 891\n", + "\n", + "CaraBitcoin - 100000 - 1000\n", + "\n", + "Antonio09232416 - 5 - 61\n", + "\n", + "StaidlerJoao - 100000 - 1000\n", + "\n", + "Douglla92210910 - 8 - 107\n", + "\n", + "Emerson93983042 - 3 - 58\n", + "\n", + "Tatiana90234871 - 3 - 44\n", + "\n", + "ppereznit - 3 - 59\n", + "\n", + "amflyhorse - 2 - 10\n", + "\n", + "claudia77459463 - 7 - 71\n", + "\n", + "FernandaGondi15 - 2 - 1265\n", + "\n", + "EdmarNonaConta - 100000 - 1000\n", + "\n", + "Helena2015sc - 2 - 42\n", + "\n", + "Marciomac21 - 0 - 50\n", + "\n", + "whythyg - 100000 - 1000\n", + "\n", + "CidaInacio3 - 100000 - 1000\n", + "\n", + "LDLourenco1 - 5 - 214\n", + "\n", + "MariaHN177 - 10 - 68\n", + "\n", + "crioulossantafe - 11 - 96\n", + "\n", + "aurelio_cuneo - 3 - 50\n", + "\n", + "SuelyLo36228465 - 0 - 41\n", + "\n", + "CeliaLuz8 - 7 - 30\n", + "\n", + "angelstrachulsk - 2 - 32\n", + "\n", + "V69765183 - 2 - 57\n", + "\n", + "junh51 - 100000 - 1000\n", + "\n", + "CarlosHalberto - 3 - 15\n", + "\n", + "SauloHenriqueC5 - 3 - 17\n", + "\n", + "Augusto40658681 - 3 - 30\n", + "\n", + "MFernades1 - 3 - 273\n", + "\n", + "malexandre0177 - 0 - 17\n", + "\n", + "belluccis - 4 - 26\n", + "\n", + "Crist__Barros - 100000 - 1000\n", + "\n", + "CarmelindaSi - 5 - 35\n", + "\n", + "jmarquesnit - 4 - 17\n", + "\n", + "leilatrs46 - 5 - 33\n", + "\n", + "paulofs2002 - 3 - 16\n", + "\n", + "JohnTrolliano - 100000 - 1000\n", + "\n", + "ngeloCorrea1 - 3 - 59\n", + "\n", + "LuizFer21857553 - 3 - 134\n", + "\n", + "LenniMarqs - 1 - 8\n", + "\n", + "marciavrr - 10 - 127\n", + "\n", + "LikaDrew1 - 2 - 7\n", + "\n", + "ane_meneghine - 7 - 174\n", + "\n", + "CarlosR95416107 - 1 - 61\n", + "\n", + "nato_observador - 100000 - 1000\n", + "\n", + "oscarcapella - 2 - 13\n", + "\n", + "LourivalFontes - 3 - 163\n", + "\n", + "Naval_38 - 100000 - 1000\n", + "\n", + "Costajudy2 - 3 - 81\n", + "\n", + "Clausag - 3 - 23\n", + "\n", + "lijolie3d - 4 - 10\n", + "\n", + "josaxl - 5 - 90\n", + "\n", + "sedi_moriw - 100000 - 1000\n", + "\n", + "Neto91591891 - 1 - 202\n", + "\n", + "cristy04252955 - 9 - 228\n", + "\n", + "marycomentts - 3 - 76\n", + "\n", + "joseluisfreita2 - 44 - 3293\n", + "\n", + "JosHenr34743855 - 7 - 28\n", + "\n", + "Mariajo66342095 - 3 - 57\n", + "\n", + "fool4reki - 100000 - 1000\n", + "\n", + "Illydio9 - 100000 - 1000\n", + "\n", + "LESB0REKO - 100000 - 1000\n", + "\n", + "tonhaomarreta - 100000 - 1000\n", + "\n", + "animesFaFanatic - 100000 - 1000\n", + "\n", + "JosyBarreto17 - 0 - 53\n", + "\n", + "KITS3MI - 100000 - 1000\n", + "\n", + "jorgelu22463996 - 3 - 28\n", + "\n", + "Dementador16 - 7 - 998\n", + "\n", + "Reallytando - 3 - 15\n", + "\n", + "RabeloGiuliano - 33 - 19095\n", + "\n", + "B38Bolsonaro - 3 - 62\n", + "\n", + "ericayhwh - 0 - 24\n", + "\n", + "Gui_BR_USA - 1 - 29\n", + "\n", + "BrunaVa25912175 - 3 - 20\n", + "\n", + "CitardaSandra - 4 - 499\n", + "\n", + "larimont00 - 2 - 13\n", + "\n", + "ZSantossaz - 100000 - 1000\n", + "\n", + "bosswooy - 100000 - 1000\n", + "\n", + "BolsonaroMessi4 - 0 - 31\n", + "\n", + "daysecoficial - 0 - 62\n", + "\n", + "kthdetaills - 100000 - 1000\n", + "\n", + "Marcelo84048142 - 100000 - 1000\n", + "\n", + "guilher81248250 - 9 - 38\n", + "\n", + "Paulolo98015633 - 18 - 98\n", + "\n", + "PauloMBrasil - 13 - 169\n", + "\n", + "jsgirl - 100000 - 1000\n", + "\n", + "AvilaAlcindo - 2 - 35\n", + "\n", + "bruno64855465 - 100000 - 1000\n", + "\n", + "BielFenix7 - 1 - 22\n", + "\n", + "3nzW0ooo - 100000 - 1000\n", + "\n", + "deizetodoroki - 10 - 196\n", + "\n", + "Alvonaro - 100000 - 1000\n", + "\n", + "thome_edna - 8 - 667\n", + "\n", + "Dr_Jr_Sarilho - 0 - 236\n", + "\n", + "PaulinhaLandimd - 100000 - 1000\n", + "\n", + "joseantoniomas7 - 2 - 20\n", + "\n", + "CesarQuental - 3 - 39\n", + "\n", + "Littlezinho_ - 5 - 153\n", + "\n", + "AmandaF97806554 - 5 - 125\n", + "\n", + "galati_orlando - 6 - 24\n", + "\n", + "JAPONESA35 - 1 - 10\n", + "\n", + "WaldemirodaSil4 - 14 - 98\n", + "\n", + "lucia7Olivei - 6 - 249\n", + "\n", + "diorknjm - 100000 - 1000\n", + "\n", + "Rosemar71665199 - 100000 - 1000\n", + "\n", + "Ariranha14 - 6 - 118\n", + "\n", + "bolso_nina - 9 - 76\n", + "\n", + "deboras36903972 - 8 - 135\n", + "\n", + "JoseNet60444277 - 100000 - 1000\n", + "\n", + "DolacioMendes - 2 - 23\n", + "\n", + "samuellelis9 - 2 - 13\n", + "\n", + "zeplu1 - 3 - 23\n", + "\n", + "fatimadocarmo3 - 2 - 24\n", + "\n", + "Davi74368318 - 0 - 38\n", + "\n", + "DjalvaDos - 10 - 78\n", + "\n", + "podroes - 1 - 31\n", + "\n", + "wolfjorge201 - 3 - 17\n", + "\n", + "MagnoLM_3 - 2 - 6\n", + "\n", + "HasselMendes - 6 - 29\n", + "\n", + "reginaveiga15 - 3 - 21\n", + "\n", + "ViniBonoro - 100000 - 1000\n", + "\n", + "Rosilandia11 - 4 - 31\n", + "\n", + "NayaraS64207791 - 4 - 129\n", + "\n", + "GalanteTanaka - 14 - 155\n", + "\n", + "Celi74253390 - 4 - 28\n", + "\n", + "AdilsonAdaRoch1 - 2 - 58\n", + "\n", + "Izabel93470946 - 5 - 49\n", + "\n", + "cosmikyun - 2 - 100\n", + "\n", + "AbreuJeanne - 6 - 26\n", + "\n", + "JC_Silva11 - 4 - 54\n", + "\n", + "Confiante3 - 100000 - 1000\n", + "\n", + "jaqsan1 - 2 - 39\n", + "\n", + "MariaDeFtimaMB1 - 4 - 58\n", + "\n", + "mrsouzar - 4 - 60\n", + "\n", + "Ricardo60025842 - 2 - 29\n", + "\n", + "RobsonWiller3 - 5 - 505\n", + "\n", + "MonicaViegas9 - 1 - 48\n", + "\n", + "AndreaMito20222 - 0 - 25\n", + "\n", + "ChampionZelda - 2 - 93\n", + "\n", + "celso_takeuchi - 3 - 24\n", + "\n", + "Maria89455698 - 100000 - 1000\n", + "\n", + "suzeidi2 - 100000 - 1000\n", + "\n", + "Maria67128046 - 4 - 10\n", + "\n", + "MSuprir1 - 5 - 53\n", + "\n", + "fariasbolsogata - 100000 - 1000\n", + "\n", + "LucimarAssumpo1 - 5 - 179\n", + "\n", + "jrobertogarcia - 1 - 24\n", + "\n", + "EdilsonGeremias - 2 - 40\n", + "\n", + "WJO_SP - 4 - 38\n", + "\n", + "CEBOLAZOUK - 3 - 26\n", + "\n", + "BonoroDiana - 100000 - 1000\n", + "\n", + "abaduartemo - 7 - 35\n", + "\n", + "PSIQEDU - 5 - 14\n", + "\n", + "MarcelodeResen2 - 2 - 60\n", + "\n", + "ElianeRomilce - 7 - 54\n", + "\n", + "Michael81672035 - 5 - 40\n", + "\n", + "lu_cssa - 2 - 17\n", + "\n", + "Wagner_Dablioge - 5 - 48\n", + "\n", + "SOLANGE58156854 - 4 - 25\n", + "\n", + "VANDERLEIEEDNA - 4 - 12\n", + "\n", + "rogerio_rsf - 4 - 38\n", + "\n", + "DilsonJusticei1 - 5 - 29\n", + "\n", + "zaninmoreira - 100000 - 1000\n", + "\n", + "RenatoBeck5 - 100000 - 1000\n", + "\n", + "BIGBUNDABRAS1L - 100000 - 1000\n", + "\n", + "Aline91471485 - 6 - 70\n", + "\n", + "IvanNog27975064 - 5 - 102\n", + "\n", + "SandroAvi - 2 - 57\n", + "\n", + "ShyPatriota - 3 - 103\n", + "\n", + "Roberto72531671 - 5 - 29\n", + "\n", + "DumontHiei - 0 - 119\n", + "\n", + "thomasalgarvio - 2 - 10\n", + "\n", + "Diego78409349 - 10 - 2466\n", + "\n", + "EdsonOl32246379 - 4 - 82\n", + "\n", + "MarcioArruda83 - 2 - 54\n", + "\n", + "MariaLuizaNo1 - 6 - 87\n", + "\n", + "AndersonLins8 - 3 - 23\n", + "\n", + "imnotphill - 100000 - 1000\n", + "\n", + "MariaCr70515383 - 13 - 163\n", + "\n", + "AnderVareja17 - 2 - 26\n", + "\n", + "armasemotos - 100000 - 1000\n", + "\n", + "Genunca - 4 - 16\n", + "\n", + "Keyte63748034 - 14 - 58\n", + "\n", + "edilton_costa - 7 - 93\n", + "\n", + "jamirslima - 3 - 66\n", + "\n", + "JosFlvioFerrei2 - 5 - 15\n", + "\n", + "agnaldo_vertes - 9 - 259\n", + "\n", + "LuisAst10 - 2 - 22\n", + "\n", + "franceswuetto - 8 - 92\n", + "\n", + "JorgeSi71261691 - 3 - 687\n", + "\n", + "AntunesEllison - 3 - 40\n", + "\n", + "mmenezes176 - 3 - 15\n", + "\n", + "solange_elisa - 4 - 72\n", + "\n", + "galoconserv1908 - 3 - 44\n", + "\n", + "InubiaCotias - 2 - 114\n", + "\n", + "alissonpantuza - 7 - 37\n", + "\n", + "Rf4And - 4 - 13\n", + "\n", + "AntonyGafa - 0 - 17\n", + "\n", + "jornaldalua - 5 - 223\n", + "\n", + "RNagahata - 4 - 49\n", + "\n", + "CascianoVidal - 0 - 16\n", + "\n", + "A_Bem_d_Verdade - 100000 - 1000\n", + "\n", + "VideoRevista - 100000 - 1000\n", + "\n", + "CarlosF04584828 - 8 - 163\n", + "\n", + "JooMaia20648391 - 9 - 46\n", + "\n", + "IzabelLins8 - 100000 - 1000\n", + "\n", + "RCNPATRIOTA - 100000 - 1000\n", + "\n", + "Fernand28417319 - 1 - 18\n", + "\n", + "filvjm - 1 - 2.0\n", + "\n", + "flaviovaledoaco - 2 - 40\n", + "\n", + "logaritmo_vesgo - 100000 - 1000\n", + "\n", + "Jbolso26 - 100000 - 1000\n", + "\n", + "JabalSonaro - 1 - 21\n", + "\n", + "ktsantos4 - 2 - 125\n", + "\n", + "pettsu_ss - 3 - 25\n", + "\n", + "i2gon - 100000 - 1000\n", + "\n", + "BCocovick - 11 - 4742\n", + "\n", + "RosngelaAlleva2 - 6 - 55\n", + "\n", + "JBBr26 - 100000 - 1000\n", + "\n", + "searchkaiser - 7 - 216\n", + "\n", + "BrasilLibre - 0 - 70\n", + "\n", + "Marcio_Opressor - 100000 - 1000\n", + "\n", + "marcosmmmatias - 4 - 9401\n", + "\n", + "ClaudioLuizII - 100000 - 1000\n", + "\n", + "AMessMixer - 100000 - 1000\n", + "\n", + "Ronaldo90533595 - 100000 - 1000\n", + "\n", + "PauloFr42424097 - 1 - 175\n", + "\n", + "das_diogo - 2 - 36\n", + "\n", + "RlcardoLima1234 - 3 - 35\n", + "\n", + "SonalKu91552413 - 0 - 6\n", + "\n", + "ValLiesner - 100000 - 1000\n", + "\n", + "bolsonarita - 100000 - 1000\n", + "\n", + "HamiltonHirai - 100000 - 1000\n", + "\n", + "poderparalelo40 - 5 - 63\n", + "\n", + "louisthegnomo - 2 - 254\n", + "\n", + "Mirandamito2026 - 25 - 298\n", + "\n", + "fbpborges - 100000 - 1000\n", + "\n", + "AlexandreSDR1 - 1 - 1140\n", + "\n", + "BeatrizBeaPrad1 - 12 - 52\n", + "\n", + "martinesconserv - 5 - 31\n", + "\n", + "risquinhorisco - 8 - 55\n", + "\n", + "Dama_aco - 100000 - 1000\n", + "\n", + "Deslacrando2 - 100000 - 1000\n", + "\n", + "AndreaA00163221 - 2 - 173\n", + "\n", + "RosieRubyLisaSY - 100000 - 1000\n", + "\n", + "ZechCoelho - 1 - 91\n", + "\n", + "kdramaxthiana - 100000 - 1000\n", + "\n", + "AdrianaR943 - 2 - 62\n", + "\n", + "XeroxHolmesMG - 100000 - 1000\n", + "\n", + "_rosy_38 - 2 - 25\n", + "\n", + "JORNALBRASILDE1 - 0 - 237\n", + "\n", + "ssa_mariana - 14 - 258\n", + "\n", + "luciano280611 - 4 - 14\n", + "\n", + "BazilioAlex - 100000 - 1000\n", + "\n", + "Ruineves27 - 5 - 109\n", + "\n", + "dixxyugyeom - 3 - 40\n", + "\n", + "Prestonejr - 2 - 38\n", + "\n", + "bysoocut - 100000 - 1000\n", + "\n", + "PG21A - 100000 - 1000\n", + "\n", + "sabricream - 3 - 104\n", + "\n", + "favboyjinnie - 0 - 419\n", + "\n", + "Everton__080820 - 21 - 3959\n", + "\n", + "comentobbb2021 - 18 - 414\n", + "\n", + "LauraSi66030021 - 8 - 163\n", + "\n", + "Severin11496561 - 5 - 72\n", + "\n", + "semfenoBrasil - 100000 - 1000\n", + "\n", + "Cleiton58410322 - 3 - 128\n", + "\n", + "Suiane12406352 - 1 - 24\n", + "\n", + "Solange40232969 - 5 - 31\n", + "\n", + "3li4stt - 100000 - 1000\n", + "\n", + "JosDaSi22960290 - 2 - 7730\n", + "\n", + "Alamo21801113 - 8 - 72\n", + "\n", + "VanderM43500386 - 3 - 8\n", + "\n", + "SHYMIY4 - 100000 - 1000\n", + "\n", + "Celia71535963 - 0 - 136\n", + "\n", + "conta2412 - 0 - 52\n", + "\n", + "NelsonG69003970 - 10 - 161\n", + "\n", + "Brasil53565802 - 18 - 130\n", + "\n", + "Ranieri10690411 - 3 - 84\n", + "\n", + "RoS3bas - 14 - 147\n", + "\n", + "Marcelo20488928 - 0 - 57\n", + "\n", + "MariaSi38578012 - 100000 - 1000\n", + "\n", + "Mariado74196920 - 1 - 22\n", + "\n", + "Mariada01006622 - 3 - 86\n", + "\n", + "joaomar56345454 - 1 - 37\n", + "\n", + "onconaras - 1 - 159\n", + "\n", + "MARIAde46892798 - 5 - 44\n", + "\n", + "ita_brirkm - 100000 - 1000\n", + "\n", + "Luciana63518874 - 100000 - 1000\n", + "\n", + "Romeno41520857 - 9 - 105\n", + "\n", + "eliz26204429 - 1 - 28\n", + "\n", + "Sandrs98503463 - 2 - 21\n", + "\n", + "herminho1968 - 12 - 267\n", + "\n", + "Nakache89149087 - 100000 - 1000\n", + "\n", + "Cludia88041988 - 100000 - 1000\n", + "\n", + "Cesar3865086216 - 4 - 58\n", + "\n", + "Marilen18048909 - 0 - 44\n", + "\n", + "Ny19701801 - 4 - 64\n", + "\n", + "LUCIAAP54468663 - 5 - 37\n", + "\n", + "well60156361 - 100000 - 1000\n", + "\n", + "marcope52621610 - 4 - 24\n", + "\n", + "PauloEd41989183 - 3 - 28\n", + "\n", + "FranzeNicolina - 6 - 100\n", + "\n", + "ZAI24379140 - 4 - 58\n", + "\n", + "Marcelo88387147 - 1 - 102\n", + "\n", + "Agnaldo34941846 - 3 - 39\n", + "\n", + "Centuri41653728 - 2 - 82\n", + "\n", + "Cristin57751541 - 4 - 540\n", + "\n", + "marcos81015111 - 4 - 42\n", + "\n", + "PedroRo20022843 - 5 - 24\n", + "\n", + "RightwayLeo - 3 - 128\n", + "\n", + "Maria35953626 - 4 - 628\n", + "\n", + "AnaOliv48115641 - 0 - 21\n", + "\n", + "messiasdireito - 4 - 133\n", + "\n", + "Brasil37931157 - 100000 - 1000\n", + "\n", + "AnnaVie62574062 - 6 - 37\n", + "\n", + "EdeRS54160658 - 100000 - 1000\n", + "\n", + "httpmyprecious - 10 - 2226\n", + "\n", + "azul963963 - 4 - 33\n", + "\n", + "Fatima80125994 - 6 - 200\n", + "\n", + "B4lVOa18c8WF7ks - 7 - 218\n", + "\n", + "RIntegrarh - 7 - 47\n", + "\n", + "malu17458230 - 100000 - 1000\n", + "\n", + "AnglicaMar1 - 10 - 137\n", + "\n", + "Fernand98756869 - 3 - 184\n", + "\n", + "Caliba29505144 - 6 - 77\n", + "\n", + "Calixto53145354 - 2 - 207\n", + "\n", + "SandraP82614540 - 7 - 47\n", + "\n", + "Marcoan28028309 - 5 - 122\n", + "\n", + "EsquerdopataK - 100000 - 1000\n", + "\n", + "Older16139373 - 100000 - 1000\n", + "\n", + "Pedro57245921 - 5 - 124\n", + "\n", + "95_taehyng - 100000 - 1000\n", + "\n", + "Daniel52533649 - 5 - 83\n", + "\n", + "Irissou05792611 - 100000 - 1000\n", + "\n", + "Carlos91433342 - 3 - 28\n", + "\n", + "JooBosc33631608 - 3 - 40\n", + "\n", + "Anderso18742140 - 5 - 46\n", + "\n", + "eugenioazambuja - 12 - 80\n", + "\n", + "gizcrf_ - 2 - 29\n", + "\n", + "TiaLu47784157 - 100000 - 1000\n", + "\n", + "Antnio86965451 - 23 - 800\n", + "\n", + "DanielF91125971 - 0 - 74\n", + "\n", + "Fabiopf08Fabio - 2 - 6\n", + "\n", + "Contreiras2016 - 1 - 23\n", + "\n", + "AnaPere43116126 - 100000 - 1000\n", + "\n", + "Marilen20674578 - 8 - 53\n", + "\n", + "PolaDaSilva3 - 7 - 220\n", + "\n", + "florfernandes16 - 4 - 158\n", + "\n", + "Ryujapan2020BR - 100000 - 1000\n", + "\n", + "joao_merisio - 3 - 69\n", + "\n", + "DsclpPorSerBot - 60 - 120\n", + "\n", + "SandySi67309463 - 4 - 26\n", + "\n", + "Yellowbird77777 - 100000 - 1000\n", + "\n", + "mariame77095295 - 100000 - 1000\n", + "\n", + "LeoniceMariana1 - 8 - 181\n", + "\n", + "caguei_morango - 6 - 186\n", + "\n", + "jluqueiroz - 3 - 46\n", + "\n", + "CalvoLuzia - 7 - 32\n", + "\n", + "ReginaC60159030 - 2 - 40\n", + "\n", + "chaar_marcos - 2 - 15\n", + "\n", + "MacielMouraDaC2 - 0 - 38\n", + "\n", + "Cinthiaperdiga4 - 0 - 148\n", + "\n", + "LidiaConta - 2 - 16\n", + "\n", + "CleuzaAlbuquer2 - 5 - 290\n", + "\n", + "Hemmer1111 - 5 - 61\n", + "\n", + "withloveIarry - 100000 - 1000\n", + "\n", + "BrisaBot - 100000 - 1000\n", + "\n", + "Cludia83802707 - 3 - 11\n", + "\n", + "marry4142 - 4 - 57\n", + "\n", + "1976Mnc - 5 - 63\n", + "\n", + "MariaRo46506601 - 3 - 22\n", + "\n", + "carnielli_lucio - 5 - 47\n", + "\n", + "yoshio_carlos - 0 - 68\n", + "\n", + "SantanaRC2 - 100000 - 1000\n", + "\n", + "MARIOVIEIRA007 - 1 - 27\n", + "\n", + "ismarckel1 - 2 - 14\n", + "\n", + "midoriy4__ - 100000 - 1000\n", + "\n", + "GauchoLuisinho - 5 - 136\n", + "\n", + "ElizabethGalo18 - 100000 - 1000\n", + "\n", + "MrciaSampaioMo2 - 4 - 21\n", + "\n", + "ruisaldanha - 6 - 27\n", + "\n", + "suzetesouza5 - 2 - 16\n", + "\n", + "UnbornLiberty - 100000 - 1000\n", + "\n", + "Sheila55368560 - 2 - 24\n", + "\n", + "Herval68461744 - 2 - 21\n", + "\n", + "EdsonMa37160190 - 100000 - 1000\n", + "\n", + "jenngyus - 100000 - 1000\n", + "\n", + "BastosJusmari - 2 - 22\n", + "\n", + "roweIine - 6 - 130\n", + "\n", + "Elevenceyvence - 0 - 29\n", + "\n", + "Naoseinadaa_ - 100000 - 1000\n", + "\n", + "CunhaLucare - 1 - 56\n", + "\n", + "bonnieximena - 0 - 12\n", + "\n", + "CirlenePonce - 3 - 20\n", + "\n", + "PauloFerdinand5 - 2 - 32\n", + "\n", + "zemariasccp1 - 3 - 59\n", + "\n", + "PatriotaConser4 - 100000 - 1000\n", + "\n", + "Patrcia98461331 - 100000 - 1000\n", + "\n", + "tovaga - 4 - 15\n", + "\n", + "PaganiBibi - 6 - 51\n", + "\n", + "AugustoBrando10 - 2 - 18\n", + "\n", + "MariaAp76712592 - 2 - 50\n", + "\n", + "luizmartelo359 - 3 - 40\n", + "\n", + "GaleanoRonie - 100000 - 1000\n", + "\n", + "CelsoRock1 - 3 - 288\n", + "\n", + "Karine10268893 - 4 - 20\n", + "\n", + "DelsonGonalves9 - 3 - 14\n", + "\n", + "rjricardinho - 3 - 20\n", + "\n", + "PerreiraNilo - 8 - 105\n", + "\n", + "MauroMo83249655 - 100000 - 1000\n", + "\n", + "Careca95342400 - 100000 - 1000\n", + "\n", + "PATRIOTABR18 - 100000 - 1000\n", + "\n", + "SunTzu72703894 - 100000 - 1000\n", + "\n", + "IsraelN93579039 - 2 - 14\n", + "\n", + "MarciaSAlmeida - 8 - 38\n", + "\n", + "CelsoCosta38 - 100000 - 1000\n", + "\n", + "fabiss1072 - 4 - 87\n", + "\n", + "AnaMBolsonaro - 100000 - 1000\n", + "\n", + "F4810_0 - 100000 - 1000\n", + "\n", + "ingrid_maria_57 - 100000 - 1000\n", + "\n", + "CarlosA76979782 - 2 - 7\n", + "\n", + "CabralMakiyoshi - 0 - 194\n", + "\n", + "Guilhe22821397 - 6 - 55\n", + "\n", + "PauloCzardaSil2 - 100000 - 1000\n", + "\n", + "AlexandreRicht7 - 7 - 162\n", + "\n", + "ricardomelo1968 - 100000 - 1000\n", + "\n", + "DilaSoares9 - 1 - 5\n", + "\n", + "NeusaMarquess - 0 - 21\n", + "\n", + "ReginaConte3 - 10 - 279\n", + "\n", + "RenataMartinsR3 - 0 - 43\n", + "\n", + "Antonio24529020 - 0 - 23\n", + "\n", + "NATHANI18729150 - 100000 - 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": 372, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "id": "ppTFMTsTvEnq", + "outputId": "86f70607-e914-4bae-8659-b0edd9c98e68" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1072\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? ... followers_count friends_count lang \\\n", + "0 não ... 21.0 108.0 0.0 \n", + "\n", + " location name \\\n", + "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 \n", + "0 0.0 0.0 [] \n", + "\n", + "[1 rows x 34 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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[]
\n", + "

1 rows × 34 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 372 + } + ], + "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(1)" + ] + }, + { + "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": 373, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 223 + }, + "id": "uJXpyQCrvEnr", + "outputId": "1dcc1393-e022-490d-efd8-0925fb258617" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "835\n" + ] + }, + { + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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 ...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 373 + } + ], + "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()\n", + "print(len(df_result_text))\n", + "df_result_text.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 374, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "Im7H7kcxvEnr", + "outputId": "388dad85-45cb-4a34-f95d-ab370784a70d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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..." + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
tweet_authortweet_hashtags
0100_bolsonaro['Bolsonaro2022'], ['MoroTraidor'], [], ['Moro...
113valber1[], [], [], [], [], [], [], [], [], [], [], []...
21976Mnc[], [], [], [], [], [], ['PLP235NÃO'], [], ['P...
3ACamargo241[], [], [], [], [], [], [], [], [], [], [], []...
4AControld['RedeBBB', 'tbt', 'iphone', 'apple'], ['Natal...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 374 + } + ], + "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": 375, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "6LSMR2a_vEnr", + "outputId": "72fe2935-dee4-4258-c84b-435452114d8b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " tweet_author tweet_source\n", + "0 100_bolsonaro Twitter Web App, Twitter Web App, Twitter Web ...\n", + "1 13valber1 Twitter for Android, Twitter for Android, Twit...\n", + "2 1976Mnc Twitter for iPhone, Twitter for iPhone, Twitte...\n", + "3 ACamargo241 Twitter for Android, Twitter for Android, Twit...\n", + "4 AControld Twitter Web App, Twitter Web App, Twitter Web ..." + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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 ...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 375 + } + ], + "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()\n", + "df_result_source.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "ea4RsnYvvEnr", + "outputId": "cfb70109-e2d9-46b1-c04c-d73cdb12a581" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " tweet_author retweet_tratado\n", + "0 100_bolsonaro não, não, não, não, não, não, não, não, não, n...\n", + "1 13valber1 não, não, não, não, não, não, não, não, não, n...\n", + "2 1976Mnc não, não, não, não, não, não, não, não, não, n...\n", + "3 ACamargo241 não, não, não, não, não, sim, não, não, não, n...\n", + "4 AControld não, não, não, não, não, não, não, não, não, n..." + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 376 + } + ], + "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": 379, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "baeAt5qkvEns", + "outputId": "f91e849c-e0c6-4144-a1f3-e3d52edf3367" + }, + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 379 + } + ], + "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": 378, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "zkPS0tjzvEns", + "outputId": "23721efc-985b-4af0-e79f-ce280cac0e0b" + }, + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 378 + } + ], + "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": 380, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZwA3QA7dvEns", + "outputId": "0290d111-7b62-45ad-9ca8-3cc98907573c" + }, + "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": 381, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "DdtIwKDhvEnt", + "outputId": "4e6f8923-1003-499f-e0a3-513c9fb55e4d" + }, + "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? ... 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]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "

1 rows × 46 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 381 + } + ], + "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": 382, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-6hG03d0vEnt", + "outputId": "dda2efeb-d532-411c-b1a5-6f1c10f3a74c" + }, + "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": 382 + } + ], + "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": 383, + "metadata": { + "id": "Tsho3SNYvEnt", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "425dcc2e-b546-4d44-f083-8402fa04cddd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(834,)" + ] + }, + "metadata": {}, + "execution_count": 383 + } + ], + "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": 384, + "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": 385, + "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": 386, + "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": 389, + "metadata": { + "id": "xyrFTNw-vEnu" + }, + "outputs": [], + "source": [ + "#text_mnb_stemmed" + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "metadata": { + "id": "teRgHViCvEnu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b2647480-83fb-40d2-aa56-38608ba306f5" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.7171314741035857" + ] + }, + "metadata": {}, + "execution_count": 388 + } + ], + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "

1 rows × 46 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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 \n", + "0 0.00 \n", + "1 0.76 \n", + "2 0.00 \n", + "3 0.00 \n", + "4 0.00 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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 \n", + "0 0.840000 \n", + "1 0.970000 \n", + "2 0.910000 \n", + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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-
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "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! 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 - " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Busca os dados de todas as trending topics recuperadas\n", + "datafile_trends = \"/content/sample_data/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": { + "id": "TNnE-brVvEn3" + }, + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 739 + }, + "id": "kD0Zkg4ZvEn3", + "outputId": "a5861677-d6e4-4637-d7d9-ed125b69d6ff" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: 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", + " if __name__ == '__main__':\n", + "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: 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", + " self._setitem_single_block(indexer, value, name)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "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! 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", + "45\n", + "46\n", + "47\n", + "48\n", + "49\n", + "50\n", + "51\n", + "52\n", + "53\n", + "54\n", + "55\n", + "56\n", + "57\n", + "58\n", + "59\n", + "60\n", + "61\n", + "62\n", + "63\n", + "64\n", + "65\n", + "66\n", + "67\n", + "68\n", + "69\n", + "70\n", + "71\n", + "72\n", + "73\n", + "74\n", + "75\n", + "76\n", + "77\n", + "78\n", + "79\n", + "80\n", + "81\n", + "82\n", + "83\n", + "84\n", + "85\n", + "86\n", + "87\n", + "88\n", + "89\n", + "90\n", + "91\n", + "92\n", + "93\n", + "94\n", + "95\n", + "96\n", + "97\n", + "98\n", + "99\n", + "100\n", + "101\n", + "102\n", + "103\n", + "104\n", + "105\n", + "106\n", + "107\n", + "108\n", + "109\n", + "110\n", + "111\n", + "112\n", + "113\n", + "114\n", + "115\n", + "116\n", + "117\n", + "118\n", + "119\n", + "120\n", + "121\n", + "122\n", + "123\n", + "124\n", + "125\n", + "126\n", + "127\n", + "128\n", + "129\n", + "130\n", + "131\n", + "132\n", + "133\n", + "134\n", + "135\n", + "136\n", + "137\n", + "138\n", + "139\n", + "140\n", + "141\n", + "142\n", + "143\n", + "144\n", + "145\n", + "146\n", + "147\n", + "148\n", + "149\n", + "150\n", + "151\n", + "152\n", + "153\n", + "154\n", + "155\n", + "156\n", + "157\n", + "158\n", + "159\n", + "160\n", + "161\n", + "162\n", + "163\n", + "164\n", + "165\n", + "166\n", + "167\n", + "168\n", + "169\n", + "170\n", + "171\n", + "172\n", + "173\n", + "174\n", + "175\n", + "176\n", + "177\n", + "178\n", + "179\n", + "180\n", + "181\n", + "182\n", + "183\n", + "184\n", + "185\n", + "186\n", + "187\n", + "188\n", + "189\n", + "190\n", + "191\n", + "192\n", + "193\n", + "194\n", + "195\n", + "196\n", + "197\n", + "198\n", + "199\n", + "200\n", + "201\n", + "202\n", + "203\n", + "204\n", + "205\n", + "206\n", + "207\n", + "208\n", + "209\n", + "210\n", + "211\n", + "212\n", + "213\n", + "214\n", + "215\n", + "216\n", + "217\n", + "218\n", + "219\n", + "220\n", + "221\n", + "222\n", + "223\n", + "224\n", + "225\n", + "226\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: 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", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;30;43mA saída de streaming foi truncada nas últimas 5000 linhas.\u001b[0m\n", + "77413\n", + "77414\n", + "77415\n", + "77416\n", + "77417\n", + "77418\n", + "77419\n", + "77420\n", + "77421\n", + "77422\n", + "77423\n", + "77424\n", + "77425\n", + "77426\n", + "77427\n", + "77428\n", + "77429\n", + "77430\n", + "77431\n", + "77432\n", + "77433\n", + "77434\n", + "77435\n", + "77436\n", + "77437\n", + "77438\n", + "77439\n", + "77440\n", + "77441\n", + "77442\n", + "77443\n", + "77444\n", + "77445\n", + "77446\n", + "77447\n", + "77448\n", + "77449\n", + "77450\n", + "77451\n", + "77452\n", + "77453\n", + "77454\n", + "77455\n", + "77456\n", + "77457\n", + "77458\n", + "77459\n", + "77460\n", + "77461\n", + "77462\n", + "77463\n", + "77464\n", + "77465\n", + "77466\n", + "77467\n", + "77468\n", + "77469\n", + "77470\n", + "77471\n", + "77472\n", + "77473\n", + "77474\n", + "77475\n", + "77476\n", + "77477\n", + "77478\n", + "77479\n", + "77480\n", + "77481\n", + "77482\n", + "77483\n", + "77484\n", + "77485\n", + "77486\n", + "77487\n", + "77488\n", + "77489\n", + "77490\n", + "77491\n", + "77492\n", + "77493\n", + "77494\n", + "77495\n", + "77496\n", + "77497\n", + "77498\n", + "77499\n", + "77500\n", + "77501\n", + "77502\n", + "77503\n", + "77504\n", + "77505\n", + "77506\n", + "77507\n", + "77508\n", + "77509\n", + "77510\n", + "77511\n", + "77512\n", + "77513\n", + "77514\n", + "77515\n", + "77516\n", + "77517\n", + "77518\n", + "77519\n", + "77520\n", + "77521\n", + "77522\n", + "77523\n", + "77524\n", + "77525\n", + "77526\n", + "77527\n", + "77528\n", + "77529\n", + "77530\n", + "77531\n", + "77532\n", + "77533\n", + "77534\n", + "77535\n", + "77536\n", + "77537\n", + "77538\n", + "77539\n", + "77540\n", + "77541\n", + "77542\n", + "77543\n", + "77544\n", + "77545\n", + "77546\n", + "77547\n", + "77548\n", + "77549\n", + "77550\n", + "77551\n", + "77552\n", + "77553\n", + "77554\n", + "77555\n", + "77556\n", + "77557\n", + "77558\n", + "77559\n", + "77560\n", + "77561\n", + "77562\n", + "77563\n", + "77564\n", + "77565\n", + "77566\n", + "77567\n", + "77568\n", + "77569\n", + "77570\n", + "77571\n", + "77572\n", + "77573\n", + "77574\n", + "77575\n", + "77576\n", + "77577\n", + "77578\n", + "77579\n", + "77580\n", + "77581\n", + "77582\n", + "77583\n", + "77584\n", + "77585\n", + "77586\n", + "77587\n", + "77588\n", + "77589\n", + "77590\n", + "77591\n", + "77592\n", + "77593\n", + "77594\n", + "77595\n", + "77596\n", + "77597\n", + "77598\n", + "77599\n", + "77600\n", + "77601\n", + "77602\n", + "77603\n", + "77604\n", + "77605\n", + "77606\n", + "77607\n", + "77608\n", + "77609\n", + "77610\n", + "77611\n", + "77612\n", + "77613\n", + "77614\n", + "77615\n", + "77616\n", + "77617\n", + "77618\n", + "77619\n", + "77620\n", + "77621\n", + "77622\n", + "77623\n", + "77624\n", + "77625\n", + "77626\n", + "77627\n", + "77628\n", + "77629\n", + "77630\n", + "77631\n", + "77632\n", + "77633\n", + "77634\n", + "77635\n", + "77636\n", + "77637\n", + "77638\n", + "77639\n", + "77640\n", + "77641\n", + "77642\n", + "77643\n", + "77644\n", + "77645\n", + "77646\n", + "77647\n", + "77648\n", + "77649\n", + "77650\n", + "77651\n", + "77652\n", + "77653\n", + "77654\n", + "77655\n", + "77656\n", + "77657\n", + "77658\n", + "77659\n", + "77660\n", + "77661\n", + "77662\n", + "77663\n", + "77664\n", + "77665\n", + "77666\n", + "77667\n", + "77668\n", + "77669\n", + "77670\n", + "77671\n", + "77672\n", + "77673\n", + "77674\n", + "77675\n", + "77676\n", + "77677\n", + "77678\n", + "77679\n", + "77680\n", + "77681\n", + "77682\n", + "77683\n", + "77684\n", + "77685\n", + "77686\n", + "77687\n", + "77688\n", + "77689\n", + "77690\n", + "77691\n", + "77692\n", + "77693\n", + "77694\n", + "77695\n", + "77696\n", + "77697\n", + "77698\n", + "77699\n", + "77700\n", + "77701\n", + "77702\n", + "77703\n", + "77704\n", + "77705\n", + "77706\n", + "77707\n", + "77708\n", + "77709\n", + "77710\n", + "77711\n", + "77712\n", + "77713\n", + "77714\n", + "77715\n", + "77716\n", + "77717\n", + "77718\n", + "77719\n", + "77720\n", + "77721\n", + "77722\n", + "77723\n", + "77724\n", + "77725\n", + "77726\n", + "77727\n", + "77728\n", + "77729\n", + "77730\n", + "77731\n", + "77732\n", + "77733\n", + "77734\n", + "77735\n", + "77736\n", + "77737\n", + "77738\n", + "77739\n", + "77740\n", + "77741\n", + "77742\n", + "77743\n", + "77744\n", + "77745\n", + "77746\n", + "77747\n", + "77748\n", + "77749\n", + "77750\n", + "77751\n", + "77752\n", + "77753\n", + "77754\n", + "77755\n", + "77756\n", + "77757\n", + "77758\n", + "77759\n", + "77760\n", + "77761\n", + "77762\n", + "77763\n", + "77764\n", + "77765\n", + "77766\n", + "77767\n", + "77768\n", + "77769\n", + "77770\n", + "77771\n", + "77772\n", + "77773\n", + "77774\n", + "77775\n", + "77776\n", + "77777\n", + "77778\n", + "77779\n", + "77780\n", + "77781\n", + "77782\n", + "77783\n", + "77784\n", + "77785\n", + "77786\n", + "77787\n", + "77788\n", + "77789\n", + "77790\n", + "77791\n", + "77792\n", + "77793\n", + "77794\n", + "77795\n", + "77796\n", + "77797\n", + "77798\n", + "77799\n", + "77800\n", + "77801\n", + "77802\n", + "77803\n", + "77804\n", + "77805\n", + "77806\n", + "77807\n", + "77808\n", + "77809\n", + "77810\n", + "77811\n", + "77812\n", + "77813\n", + "77814\n", + "77815\n", + "77816\n", + "77817\n", + "77818\n", + "77819\n", + "77820\n", + "77821\n", + "77822\n", + "77823\n", + "77824\n", + "77825\n", + "77826\n", + "77827\n", + "77828\n", + "77829\n", + "77830\n", + "77831\n", + "77832\n", + "77833\n", + "77834\n", + "77835\n", + "77836\n", + "77837\n", + "77838\n", + "77839\n", + "77840\n", + "77841\n", + "77842\n", + "77843\n", + "77844\n", + "77845\n", + "77846\n", + "77847\n", + "77848\n", + "77849\n", + "77850\n", + "77851\n", + "77852\n", + "77853\n", + "77854\n", + "77855\n", + "77856\n", + "77857\n", + "77858\n", + "77859\n", + "77860\n", + "77861\n", + "77862\n", + "77863\n", + "77864\n", + "77865\n", + "77866\n", + "77867\n", + "77868\n", + "77869\n", + "77870\n", + "77871\n", + "77872\n", + "77873\n", + "77874\n", + "77875\n", + "77876\n", + "77877\n", + "77878\n", + "77879\n", + "77880\n", + "77881\n", + "77882\n", + "77883\n", + "77884\n", + "77885\n", + "77886\n", + "77887\n", + "77888\n", + "77889\n", + "77890\n", + "77891\n", + "77892\n", + "77893\n", + "77894\n", + "77895\n", + "77896\n", + "77897\n", + "77898\n", + "77899\n", + "77900\n", + "77901\n", + "77902\n", + "77903\n", + "77904\n", + "77905\n", + "77906\n", + "77907\n", + "77908\n", + "77909\n", + "77910\n", + "77911\n", + "77912\n", + "77913\n", + "77914\n", + "77915\n", + "77916\n", + "77917\n", + "77918\n", + "77919\n", + "77920\n", + "77921\n", + "77922\n", + "77923\n", + "77924\n", + "77925\n", + "77926\n", + "77927\n", + "77928\n", + "77929\n", + "77930\n", + "77931\n", + "77932\n", + "77933\n", + "77934\n", + "77935\n", + "77936\n", + "77937\n", + "77938\n", + "77939\n", + "77940\n", + "77941\n", + "77942\n", + "77943\n", + "77944\n", + "77945\n", + "77946\n", + "77947\n", + "77948\n", + "77949\n", + "77950\n", + "77951\n", + "77952\n", + "77953\n", + "77954\n", + "77955\n", + "77956\n", + "77957\n", + "77958\n", + "77959\n", + "77960\n", + "77961\n", + "77962\n", + "77963\n", + "77964\n", + "77965\n", + "77966\n", + "77967\n", + "77968\n", + "77969\n", + "77970\n", + "77971\n", + "77972\n", + "77973\n", + "77974\n", + "77975\n", + "77976\n", + "77977\n", + "77978\n", + "77979\n", + "77980\n", + "77981\n", + "77982\n", + "77983\n", + "77984\n", + "77985\n", + "77986\n", + "77987\n", + "77988\n", + "77989\n", + "77990\n", + "77991\n", + "77992\n", + "77993\n", + "77994\n", + "77995\n", + "77996\n", + "77997\n", + "77998\n", + "77999\n", + "78000\n", + "78001\n", + "78002\n", + "78003\n", + "78004\n", + "78005\n", + "78006\n", + "78007\n", + "78008\n", + "78009\n", + "78010\n", + "78011\n", + "78012\n", + "78013\n", + "78014\n", + "78015\n", + "78016\n", + "78017\n", + "78018\n", + "78019\n", + "78020\n", + "78021\n", + "78022\n", + "78023\n", + "78024\n", + "78025\n", + "78026\n", + "78027\n", + "78028\n", + "78029\n", + "78030\n", + "78031\n", + "78032\n", + "78033\n", + "78034\n", + "78035\n", + "78036\n", + "78037\n", + "78038\n", + "78039\n", + "78040\n", + "78041\n", + "78042\n", + "78043\n", + "78044\n", + "78045\n", + "78046\n", + "78047\n", + "78048\n", + "78049\n", + "78050\n", + "78051\n", + "78052\n", + "78053\n", + "78054\n", + "78055\n", + "78056\n", + "78057\n", + "78058\n", + "78059\n", + "78060\n", + "78061\n", + "78062\n", + "78063\n", + "78064\n", + "78065\n", + "78066\n", + "78067\n", + "78068\n", + "78069\n", + "78070\n", + "78071\n", + "78072\n", + "78073\n", + "78074\n", + "78075\n", + "78076\n", + "78077\n", + "78078\n", + "78079\n", + "78080\n", + "78081\n", + "78082\n", + "78083\n", + "78084\n", + "78085\n", + "78086\n", + "78087\n", + "78088\n", + "78089\n", + "78090\n", + "78091\n", + "78092\n", + "78093\n", + "78094\n", + "78095\n", + "78096\n", + "78097\n", + "78098\n", + "78099\n", + "78100\n", + "78101\n", + "78102\n", + "78103\n", + "78104\n", + "78105\n", + "78106\n", + "78107\n", + "78108\n", + "78109\n", + "78110\n", + "78111\n", + "78112\n", + "78113\n", + "78114\n", + "78115\n", + "78116\n", + "78117\n", + "78118\n", + "78119\n", + "78120\n", + "78121\n", + "78122\n", + "78123\n", + "78124\n", + "78125\n", + "78126\n", + "78127\n", + "78128\n", + "78129\n", + "78130\n", + "78131\n", + "78132\n", + "78133\n", + "78134\n", + "78135\n", + "78136\n", + "78137\n", + "78138\n", + "78139\n", + "78140\n", + "78141\n", + "78142\n", + "78143\n", + "78144\n", + "78145\n", + "78146\n", + "78147\n", + "78148\n", + "78149\n", + "78150\n", + "78151\n", + "78152\n", + "78153\n", + "78154\n", + "78155\n", + "78156\n", + "78157\n", + "78158\n", + "78159\n", + "78160\n", + "78161\n", + "78162\n", + "78163\n", + "78164\n", + "78165\n", + "78166\n", + "78167\n", + "78168\n", + "78169\n", + "78170\n", + "78171\n", + "78172\n", + "78173\n", + "78174\n", + "78175\n", + "78176\n", + "78177\n", + "78178\n", + "78179\n", + "78180\n", + "78181\n", + "78182\n", + "78183\n", + "78184\n", + "78185\n", + "78186\n", + "78187\n", + "78188\n", + "78189\n", + "78190\n", + "78191\n", + "78192\n", + "78193\n", + "78194\n", + "78195\n", + "78196\n", + "78197\n", + "78198\n", + "78199\n", + "78200\n", + "78201\n", + "78202\n", + "78203\n", + "78204\n", + "78205\n", + "78206\n", + "78207\n", + "78208\n", + "78209\n", + "78210\n", + "78211\n", + "78212\n", + "78213\n", + "78214\n", + "78215\n", + "78216\n", + "78217\n", + "78218\n", + "78219\n", + "78220\n", + "78221\n", + "78222\n", + "78223\n", + "78224\n", + "78225\n", + "78226\n", + "78227\n", + "78228\n", + "78229\n", + "78230\n", + "78231\n", + "78232\n", + "78233\n", + "78234\n", + "78235\n", + "78236\n", + "78237\n", + "78238\n", + "78239\n", + "78240\n", + "78241\n", + "78242\n", + "78243\n", + "78244\n", + "78245\n", + "78246\n", + "78247\n", + "78248\n", + "78249\n", + "78250\n", + "78251\n", + "78252\n", + "78253\n", + "78254\n", + "78255\n", + "78256\n", + "78257\n", + "78258\n", + "78259\n", + "78260\n", + "78261\n", + "78262\n", + "78263\n", + "78264\n", + "78265\n", + "78266\n", + "78267\n", + "78268\n", + "78269\n", + "78270\n", + "78271\n", + "78272\n", + "78273\n", + "78274\n", + "78275\n", + "78276\n", + "78277\n", + "78278\n", + "78279\n", + "78280\n", + "78281\n", + "78282\n", + "78283\n", + "78284\n", + "78285\n", + "78286\n", + "78287\n", + "78288\n", + "78289\n", + "78290\n", + "78291\n", + "78292\n", + "78293\n", + "78294\n", + "78295\n", + "78296\n", + "78297\n", + "78298\n", + "78299\n", + "78300\n", + "78301\n", + "78302\n", + "78303\n", + "78304\n", + "78305\n", + "78306\n", + "78307\n", + "78308\n", + "78309\n", + "78310\n", + "78311\n", + "78312\n", + "78313\n", + "78314\n", + "78315\n", + "78316\n", + "78317\n", + "78318\n", + "78319\n", + "78320\n", + "78321\n", + "78322\n", + "78323\n", + "78324\n", + "78325\n", + "78326\n", + "78327\n", + "78328\n", + "78329\n", + "78330\n", + "78331\n", + "78332\n", + "78333\n", + "78334\n", + "78335\n", + "78336\n", + "78337\n", + "78338\n", + "78339\n", + "78340\n", + "78341\n", + "78342\n", + "78343\n", + "78344\n", + "78345\n", + "78346\n", + "78347\n", + "78348\n", + "78349\n", + "78350\n", + "78351\n", + "78352\n", + "78353\n", + "78354\n", + "78355\n", + "78356\n", + "78357\n", + "78358\n", + "78359\n", + "78360\n", + "78361\n", + "78362\n", + "78363\n", + "78364\n", + "78365\n", + "78366\n", + "78367\n", + "78368\n", + "78369\n", + "78370\n", + "78371\n", + "78372\n", + "78373\n", + "78374\n", + "78375\n", + "78376\n", + "78377\n", + "78378\n", + "78379\n", + "78380\n", + "78381\n", + "78382\n", + "78383\n", + "78384\n", + "78385\n", + "78386\n", + "78387\n", + "78388\n", + "78389\n", + "78390\n", + "78391\n", + "78392\n", + "78393\n", + "78394\n", + "78395\n", + "78396\n", + "78397\n", + "78398\n", + "78399\n", + "78400\n", + "78401\n", + "78402\n", + "78403\n", + "78404\n", + "78405\n", + "78406\n", + "78407\n", + "78408\n", + "78409\n", + "78410\n", + "78411\n", + "78412\n", + "78413\n", + "78414\n", + "78415\n", + "78416\n", + "78417\n", + "78418\n", + "78419\n", + "78420\n", + "78421\n", + "78422\n", + "78423\n", + "78424\n", + "78425\n", + "78426\n", + "78427\n", + "78428\n", + "78429\n", + "78430\n", + "78431\n", + "78432\n", + "78433\n", + "78434\n", + "78435\n", + "78436\n", + "78437\n", + "78438\n", + "78439\n", + "78440\n", + "78441\n", + "78442\n", + "78443\n", + "78444\n", + "78445\n", + "78446\n", + "78447\n", + "78448\n", + "78449\n", + "78450\n", + "78451\n", + "78452\n", + "78453\n", + "78454\n", + "78455\n", + "78456\n", + "78457\n", + "78458\n", + "78459\n", + "78460\n", + "78461\n", + "78462\n", + "78463\n", + "78464\n", + "78465\n", + "78466\n", + "78467\n", + "78468\n", + "78469\n", + "78470\n", + "78471\n", + "78472\n", + "78473\n", + "78474\n", + "78475\n", + "78476\n", + "78477\n", + "78478\n", + "78479\n", + "78480\n", + "78481\n", + "78482\n", + "78483\n", + "78484\n", + "78485\n", + "78486\n", + "78487\n", + "78488\n", + "78489\n", + "78490\n", + "78491\n", + "78492\n", + "78493\n", + "78494\n", + "78495\n", + "78496\n", + "78497\n", + "78498\n", + "78499\n", + "78500\n", + "78501\n", + "78502\n", + "78503\n", + "78504\n", + "78505\n", + "78506\n", + "78507\n", + "78508\n", + "78509\n", + "78510\n", + "78511\n", + "78512\n", + "78513\n", + "78514\n", + "78515\n", + "78516\n", + "78517\n", + "78518\n", + "78519\n", + "78520\n", + "78521\n", + "78522\n", + "78523\n", + "78524\n", + "78525\n", + "78526\n", + "78527\n", + "78528\n", + "78529\n", + "78530\n", + "78531\n", + "78532\n", + "78533\n", + "78534\n", + "78535\n", + "78536\n", + "78537\n", + "78538\n", + "78539\n", + "78540\n", + "78541\n", + "78542\n", + "78543\n", + "78544\n", + "78545\n", + "78546\n", + "78547\n", + "78548\n", + "78549\n", + "78550\n", + "78551\n", + "78552\n", + "78553\n", + "78554\n", + "78555\n", + "78556\n", + "78557\n", + "78558\n", + "78559\n", + "78560\n", + "78561\n", + "78562\n", + "78563\n", + "78564\n", + "78565\n", + "78566\n", + "78567\n", + "78568\n", + "78569\n", + "78570\n", + "78571\n", + "78572\n", + "78573\n", + "78574\n", + "78575\n", + "78576\n", + "78577\n", + "78578\n", + "78579\n", + "78580\n", + "78581\n", + "78582\n", + "78583\n", + "78584\n", + "78585\n", + "78586\n", + "78587\n", + "78588\n", + "78589\n", + "78590\n", + "78591\n", + "78592\n", + "78593\n", + "78594\n", + "78595\n", + "78596\n", + "78597\n", + "78598\n", + "78599\n", + "78600\n", + "78601\n", + "78602\n", + "78603\n", + "78604\n", + "78605\n", + "78606\n", + "78607\n", + "78608\n", + "78609\n", + "78610\n", + "78611\n", + "78612\n", + "78613\n", + "78614\n", + "78615\n", + "78616\n", + "78617\n", + "78618\n", + "78619\n", + "78620\n", + "78621\n", + "78622\n", + "78623\n", + "78624\n", + "78625\n", + "78626\n", + "78627\n", + "78628\n", + "78629\n", + "78630\n", + "78631\n", + "78632\n", + "78633\n", + "78634\n", + "78635\n", + "78636\n", + "78637\n", + "78638\n", + "78639\n", + "78640\n", + "78641\n", + "78642\n", + "78643\n", + "78644\n", + "78645\n", + "78646\n", + "78647\n", + "78648\n", + "78649\n", + "78650\n", + "78651\n", + "78652\n", + "78653\n", + "78654\n", + "78655\n", + "78656\n", + "78657\n", + "78658\n", + "78659\n", + "78660\n", + "78661\n", + "78662\n", + "78663\n", + "78664\n", + "78665\n", + "78666\n", + "78667\n", + "78668\n", + "78669\n", + "78670\n", + "78671\n", + "78672\n", + "78673\n", + "78674\n", + "78675\n", + "78676\n", + "78677\n", + "78678\n", + "78679\n", + "78680\n", + "78681\n", + "78682\n", + "78683\n", + "78684\n", + "78685\n", + "78686\n", + "78687\n", + "78688\n", + "78689\n", + "78690\n", + "78691\n", + "78692\n", + "78693\n", + "78694\n", + "78695\n", + "78696\n", + "78697\n", + "78698\n", + "78699\n", + "78700\n", + "78701\n", + "78702\n", + "78703\n", + "78704\n", + "78705\n", + "78706\n", + "78707\n", + "78708\n", + "78709\n", + "78710\n", + "78711\n", + "78712\n", + "78713\n", + "78714\n", + "78715\n", + "78716\n", + "78717\n", + "78718\n", + "78719\n", + "78720\n", + "78721\n", + "78722\n", + "78723\n", + "78724\n", + "78725\n", + "78726\n", + "78727\n", + "78728\n", + "78729\n", + "78730\n", + "78731\n", + "78732\n", + "78733\n", + "78734\n", + "78735\n", + "78736\n", + "78737\n", + "78738\n", + "78739\n", + "78740\n", + "78741\n", + "78742\n", + "78743\n", + "78744\n", + "78745\n", + "78746\n", + "78747\n", + "78748\n", + "78749\n", + "78750\n", + "78751\n", + "78752\n", + "78753\n", + "78754\n", + "78755\n", + "78756\n", + "78757\n", + "78758\n", + "78759\n", + "78760\n", + "78761\n", + "78762\n", + "78763\n", + "78764\n", + "78765\n", + "78766\n", + "78767\n", + "78768\n", + "78769\n", + "78770\n", + "78771\n", + "78772\n", + "78773\n", + "78774\n", + "78775\n", + "78776\n", + "78777\n", + "78778\n", + "78779\n", + "78780\n", + "78781\n", + "78782\n", + "78783\n", + "78784\n", + "78785\n", + "78786\n", + "78787\n", + "78788\n", + "78789\n", + "78790\n", + "78791\n", + "78792\n", + "78793\n", + "78794\n", + "78795\n", + "78796\n", + "78797\n", + "78798\n", + "78799\n", + "78800\n", + "78801\n", + "78802\n", + "78803\n", + "78804\n", + "78805\n", + "78806\n", + "78807\n", + "78808\n", + "78809\n", + "78810\n", + "78811\n", + "78812\n", + "78813\n", + "78814\n", + "78815\n", + "78816\n", + "78817\n", + "78818\n", + "78819\n", + "78820\n", + "78821\n", + "78822\n", + "78823\n", + "78824\n", + "78825\n", + "78826\n", + "78827\n", + "78828\n", + "78829\n", + "78830\n", + "78831\n", + "78832\n", + "78833\n", + "78834\n", + "78835\n", + "78836\n", + "78837\n", + "78838\n", + "78839\n", + "78840\n", + "78841\n", + "78842\n", + "78843\n", + "78844\n", + "78845\n", + "78846\n", + "78847\n", + "78848\n", + "78849\n", + "78850\n", + "78851\n", + "78852\n", + "78853\n", + "78854\n", + "78855\n", + "78856\n", + "78857\n", + "78858\n", + "78859\n", + "78860\n", + "78861\n", + "78862\n", + "78863\n", + "78864\n", + "78865\n", + "78866\n", + "78867\n", + "78868\n", + "78869\n", + "78870\n", + "78871\n", + "78872\n", + "78873\n", + "78874\n", + "78875\n", + "78876\n", + "78877\n", + "78878\n", + "78879\n", + "78880\n", + "78881\n", + "78882\n", + "78883\n", + "78884\n", + "78885\n", + "78886\n", + "78887\n", + "78888\n", + "78889\n", + "78890\n", + "78891\n", + "78892\n", + "78893\n", + "78894\n", + "78895\n", + "78896\n", + "78897\n", + "78898\n", + "78899\n", + "78900\n", + "78901\n", + "78902\n", + "78903\n", + "78904\n", + "78905\n", + "78906\n", + "78907\n", + "78908\n", + "78909\n", + "78910\n", + "78911\n", + "78912\n", + "78913\n", + "78914\n", + "78915\n", + "78916\n", + "78917\n", + "78918\n", + "78919\n", + "78920\n", + "78921\n", + "78922\n", + "78923\n", + "78924\n", + "78925\n", + "78926\n", + "78927\n", + "78928\n", + "78929\n", + "78930\n", + "78931\n", + "78932\n", + "78933\n", + "78934\n", + "78935\n", + "78936\n", + "78937\n", + "78938\n", + "78939\n", + "78940\n", + "78941\n", + "78942\n", + "78943\n", + "78944\n", + "78945\n", + "78946\n", + "78947\n", + "78948\n", + "78949\n", + "78950\n", + "78951\n", + "78952\n", + "78953\n", + "78954\n", + "78955\n", + "78956\n", + "78957\n", + "78958\n", + "78959\n", + "78960\n", + "78961\n", + "78962\n", + "78963\n", + "78964\n", + "78965\n", + "78966\n", + "78967\n", + "78968\n", + "78969\n", + "78970\n", + "78971\n", + "78972\n", + "78973\n", + "78974\n", + "78975\n", + "78976\n", + "78977\n", + "78978\n", + "78979\n", + "78980\n", + "78981\n", + "78982\n", + "78983\n", + "78984\n", + "78985\n", + "78986\n", + "78987\n", + "78988\n", + "78989\n", + "78990\n", + "78991\n", + "78992\n", + "78993\n", + "78994\n", + "78995\n", + "78996\n", + "78997\n", + "78998\n", + "78999\n", + "79000\n", + "79001\n", + "79002\n", + "79003\n", + "79004\n", + "79005\n", + "79006\n", + "79007\n", + "79008\n", + "79009\n", + "79010\n", + "79011\n", + "79012\n", + "79013\n", + "79014\n", + "79015\n", + "79016\n", + "79017\n", + "79018\n", + "79019\n", + "79020\n", + "79021\n", + "79022\n", + "79023\n", + "79024\n", + "79025\n", + "79026\n", + "79027\n", + "79028\n", + "79029\n", + "79030\n", + "79031\n", + "79032\n", + "79033\n", + "79034\n", + "79035\n", + "79036\n", + "79037\n", + "79038\n", + "79039\n", + "79040\n", + "79041\n", + "79042\n", + "79043\n", + "79044\n", + "79045\n", + "79046\n", + "79047\n", + "79048\n", + "79049\n", + "79050\n", + "79051\n", + "79052\n", + "79053\n", + "79054\n", + "79055\n", + "79056\n", + "79057\n", + "79058\n", + "79059\n", + "79060\n", + "79061\n", + "79062\n", + "79063\n", + "79064\n", + "79065\n", + "79066\n", + "79067\n", + "79068\n", + "79069\n", + "79070\n", + "79071\n", + "79072\n", + "79073\n", + "79074\n", + "79075\n", + "79076\n", + "79077\n", + "79078\n", + "79079\n", + "79080\n", + "79081\n", + "79082\n", + "79083\n", + "79084\n", + "79085\n", + "79086\n", + "79087\n", + "79088\n", + "79089\n", + "79090\n", + "79091\n", + "79092\n", + "79093\n", + "79094\n", + "79095\n", + "79096\n", + "79097\n", + "79098\n", + "79099\n", + "79100\n", + "79101\n", + "79102\n", + "79103\n", + "79104\n", + "79105\n", + "79106\n", + "79107\n", + "79108\n", + "79109\n", + "79110\n", + "79111\n", + "79112\n", + "79113\n", + "79114\n", + "79115\n", + "79116\n", + "79117\n", + "79118\n", + "79119\n", + "79120\n", + "79121\n", + "79122\n", + "79123\n", + "79124\n", + "79125\n", + "79126\n", + "79127\n", + "79128\n", + "79129\n", + "79130\n", + "79131\n", + "79132\n", + "79133\n", + "79134\n", + "79135\n", + "79136\n", + "79137\n", + "79138\n", + "79139\n", + "79140\n", + "79141\n", + "79142\n", + "79143\n", + "79144\n", + "79145\n", + "79146\n", + "79147\n", + "79148\n", + "79149\n", + "79150\n", + "79151\n", + "79152\n", + "79153\n", + "79154\n", + "79155\n", + "79156\n", + "79157\n", + "79158\n", + "79159\n", + "79160\n", + "79161\n", + "79162\n", + "79163\n", + "79164\n", + "79165\n", + "79166\n", + "79167\n", + "79168\n", + "79169\n", + "79170\n", + "79171\n", + "79172\n", + "79173\n", + "79174\n", + "79175\n", + "79176\n", + "79177\n", + "79178\n", + "79179\n", + "79180\n", + "79181\n", + "79182\n", + "79183\n", + "79184\n", + "79185\n", + "79186\n", + "79187\n", + "79188\n", + "79189\n", + "79190\n", + "79191\n", + "79192\n", + "79193\n", + "79194\n", + "79195\n", + "79196\n", + "79197\n", + "79198\n", + "79199\n", + "79200\n", + "79201\n", + "79202\n", + "79203\n", + "79204\n", + "79205\n", + "79206\n", + "79207\n", + "79208\n", + "79209\n", + "79210\n", + "79211\n", + "79212\n", + "79213\n", + "79214\n", + "79215\n", + "79216\n", + "79217\n", + "79218\n", + "79219\n", + "79220\n", + "79221\n", + "79222\n", + "79223\n", + "79224\n", + "79225\n", + "79226\n", + "79227\n", + "79228\n", + "79229\n", + "79230\n", + "79231\n", + "79232\n", + "79233\n", + "79234\n", + "79235\n", + "79236\n", + "79237\n", + "79238\n", + "79239\n", + "79240\n", + "79241\n", + "79242\n", + "79243\n", + "79244\n", + "79245\n", + "79246\n", + "79247\n", + "79248\n", + "79249\n", + "79250\n", + "79251\n", + "79252\n", + "79253\n", + "79254\n", + "79255\n", + "79256\n", + "79257\n", + "79258\n", + "79259\n", + "79260\n", + "79261\n", + "79262\n", + "79263\n", + "79264\n", + "79265\n", + "79266\n", + "79267\n", + "79268\n", + "79269\n", + "79270\n", + "79271\n", + "79272\n", + "79273\n", + "79274\n", + "79275\n", + "79276\n", + "79277\n", + "79278\n", + "79279\n", + "79280\n", + "79281\n", + "79282\n", + "79283\n", + "79284\n", + "79285\n", + "79286\n", + "79287\n", + "79288\n", + "79289\n", + "79290\n", + "79291\n", + "79292\n", + "79293\n", + "79294\n", + "79295\n", + "79296\n", + "79297\n", + "79298\n", + "79299\n", + "79300\n", + "79301\n", + "79302\n", + "79303\n", + "79304\n", + "79305\n", + "79306\n", + "79307\n", + "79308\n", + "79309\n", + "79310\n", + "79311\n", + "79312\n", + "79313\n", + "79314\n", + "79315\n", + "79316\n", + "79317\n", + "79318\n", + "79319\n", + "79320\n", + "79321\n", + "79322\n", + "79323\n", + "79324\n", + "79325\n", + "79326\n", + "79327\n", + "79328\n", + "79329\n", + "79330\n", + "79331\n", + "79332\n", + "79333\n", + "79334\n", + "79335\n", + "79336\n", + "79337\n", + "79338\n", + "79339\n", + "79340\n", + "79341\n", + "79342\n", + "79343\n", + "79344\n", + "79345\n", + "79346\n", + "79347\n", + "79348\n", + "79349\n", + "79350\n", + "79351\n", + "79352\n", + "79353\n", + "79354\n", + "79355\n", + "79356\n", + "79357\n", + "79358\n", + "79359\n", + "79360\n", + "79361\n", + "79362\n", + "79363\n", + "79364\n", + "79365\n", + "79366\n", + "79367\n", + "79368\n", + "79369\n", + "79370\n", + "79371\n", + "79372\n", + "79373\n", + "79374\n", + "79375\n", + "79376\n", + "79377\n", + "79378\n", + "79379\n", + "79380\n", + "79381\n", + "79382\n", + "79383\n", + "79384\n", + "79385\n", + "79386\n", + "79387\n", + "79388\n", + "79389\n", + "79390\n", + "79391\n", + "79392\n", + "79393\n", + "79394\n", + "79395\n", + "79396\n", + "79397\n", + "79398\n", + "79399\n", + "79400\n", + "79401\n", + "79402\n", + "79403\n", + "79404\n", + "79405\n", + "79406\n", + "79407\n", + "79408\n", + "79409\n", + "79410\n", + "79411\n", + "79412\n", + "79413\n", + "79414\n", + "79415\n", + "79416\n", + "79417\n", + "79418\n", + "79419\n", + "79420\n", + "79421\n", + "79422\n", + "79423\n", + "79424\n", + "79425\n", + "79426\n", + "79427\n", + "79428\n", + "79429\n", + "79430\n", + "79431\n", + "79432\n", + "79433\n", + "79434\n", + "79435\n", + "79436\n", + "79437\n", + "79438\n", + "79439\n", + "79440\n", + "79441\n", + "79442\n", + "79443\n", + "79444\n", + "79445\n", + "79446\n", + "79447\n", + "79448\n", + "79449\n", + "79450\n", + "79451\n", + "79452\n", + "79453\n", + "79454\n", + "79455\n", + "79456\n", + "79457\n", + "79458\n", + "79459\n", + "79460\n", + "79461\n", + "79462\n", + "79463\n", + "79464\n", + "79465\n", + "79466\n", + "79467\n", + "79468\n", + "79469\n", + "79470\n", + "79471\n", + "79472\n", + "79473\n", + "79474\n", + "79475\n", + "79476\n", + "79477\n", + "79478\n", + "79479\n", + "79480\n", + "79481\n", + "79482\n", + "79483\n", + "79484\n", + "79485\n", + "79486\n", + "79487\n", + "79488\n", + "79489\n", + "79490\n", + "79491\n", + "79492\n", + "79493\n", + "79494\n", + "79495\n", + "79496\n", + "79497\n", + "79498\n", + "79499\n", + "79500\n", + "79501\n", + "79502\n", + "79503\n", + "79504\n", + "79505\n", + "79506\n", + "79507\n", + "79508\n", + "79509\n", + "79510\n", + "79511\n", + "79512\n", + "79513\n", + "79514\n", + "79515\n", + "79516\n", + "79517\n", + "79518\n", + "79519\n", + "79520\n", + "79521\n", + "79522\n", + "79523\n", + "79524\n", + "79525\n", + "79526\n", + "79527\n", + "79528\n", + "79529\n", + "79530\n", + "79531\n", + "79532\n", + "79533\n", + "79534\n", + "79535\n", + "79536\n", + "79537\n", + "79538\n", + "79539\n", + "79540\n", + "79541\n", + "79542\n", + "79543\n", + "79544\n", + "79545\n", + "79546\n", + "79547\n", + "79548\n", + "79549\n", + "79550\n", + "79551\n", + "79552\n", + "79553\n", + "79554\n", + "79555\n", + "79556\n", + "79557\n", + "79558\n", + "79559\n", + "79560\n", + "79561\n", + "79562\n", + "79563\n", + "79564\n", + "79565\n", + "79566\n", + "79567\n", + "79568\n", + "79569\n", + "79570\n", + "79571\n", + "79572\n", + "79573\n", + "79574\n", + "79575\n", + "79576\n", + "79577\n", + "79578\n", + "79579\n", + "79580\n", + "79581\n", + "79582\n", + "79583\n", + "79584\n", + "79585\n", + "79586\n", + "79587\n", + "79588\n", + "79589\n", + "79590\n", + "79591\n", + "79592\n", + "79593\n", + "79594\n", + "79595\n", + "79596\n", + "79597\n", + "79598\n", + "79599\n", + "79600\n", + "79601\n", + "79602\n", + "79603\n", + "79604\n", + "79605\n", + "79606\n", + "79607\n", + "79608\n", + "79609\n", + "79610\n", + "79611\n", + "79612\n", + "79613\n", + "79614\n", + "79615\n", + "79616\n", + "79617\n", + "79618\n", + "79619\n", + "79620\n", + "79621\n", + "79622\n", + "79623\n", + "79624\n", + "79625\n", + "79626\n", + "79627\n", + "79628\n", + "79629\n", + "79630\n", + "79631\n", + "79632\n", + "79633\n", + "79634\n", + "79635\n", + "79636\n", + "79637\n", + "79638\n", + "79639\n", + "79640\n", + "79641\n", + "79642\n", + "79643\n", + "79644\n", + "79645\n", + "79646\n", + "79647\n", + "79648\n", + "79649\n", + "79650\n", + "79651\n", + "79652\n", + "79653\n", + "79654\n", + "79655\n", + "79656\n", + "79657\n", + "79658\n", + "79659\n", + "79660\n", + "79661\n", + "79662\n", + "79663\n", + "79664\n", + "79665\n", + "79666\n", + "79667\n", + "79668\n", + "79669\n", + "79670\n", + "79671\n", + "79672\n", + "79673\n", + "79674\n", + "79675\n", + "79676\n", + "79677\n", + "79678\n", + "79679\n", + "79680\n", + "79681\n", + "79682\n", + "79683\n", + "79684\n", + "79685\n", + "79686\n", + "79687\n", + "79688\n", + "79689\n", + "79690\n", + "79691\n", + "79692\n", + "79693\n", + "79694\n", + "79695\n", + "79696\n", + "79697\n", + "79698\n", + "79699\n", + "79700\n", + "79701\n", + "79702\n", + "79703\n", + "79704\n", + "79705\n", + "79706\n", + "79707\n", + "79708\n", + "79709\n", + "79710\n", + "79711\n", + "79712\n", + "79713\n", + "79714\n", + "79715\n", + "79716\n", + "79717\n", + "79718\n", + "79719\n", + "79720\n", + "79721\n", + "79722\n", + "79723\n", + "79724\n", + "79725\n", + "79726\n", + "79727\n", + "79728\n", + "79729\n", + "79730\n", + "79731\n", + "79732\n", + "79733\n", + "79734\n", + "79735\n", + "79736\n", + "79737\n", + "79738\n", + "79739\n", + "79740\n", + "79741\n", + "79742\n", + "79743\n", + "79744\n", + "79745\n", + "79746\n", + "79747\n", + "79748\n", + "79749\n", + "79750\n", + "79751\n", + "79752\n", + "79753\n", + "79754\n", + "79755\n", + "79756\n", + "79757\n", + "79758\n", + "79759\n", + "79760\n", + "79761\n", + "79762\n", + "79763\n", + "79764\n", + "79765\n", + "79766\n", + "79767\n", + "79768\n", + "79769\n", + "79770\n", + "79771\n", + "79772\n", + "79773\n", + "79774\n", + "79775\n", + "79776\n", + "79777\n", + "79778\n", + "79779\n", + "79780\n", + "79781\n", + "79782\n", + "79783\n", + "79784\n", + "79785\n", + "79786\n", + "79787\n", + "79788\n", + "79789\n", + "79790\n", + "79791\n", + "79792\n", + "79793\n", + "79794\n", + "79795\n", + "79796\n", + "79797\n", + "79798\n", + "79799\n", + "79800\n", + "79801\n", + "79802\n", + "79803\n", + "79804\n", + "79805\n", + "79806\n", + "79807\n", + "79808\n", + "79809\n", + "79810\n", + "79811\n", + "79812\n", + "79813\n", + "79814\n", + "79815\n", + "79816\n", + "79817\n", + "79818\n", + "79819\n", + "79820\n", + "79821\n", + "79822\n", + "79823\n", + "79824\n", + "79825\n", + "79826\n", + "79827\n", + "79828\n", + "79829\n", + "79830\n", + "79831\n", + "79832\n", + "79833\n", + "79834\n", + "79835\n", + "79836\n", + "79837\n", + "79838\n", + "79839\n", + "79840\n", + "79841\n", + "79842\n", + "79843\n", + "79844\n", + "79845\n", + "79846\n", + "79847\n", + "79848\n", + "79849\n", + "79850\n", + "79851\n", + "79852\n", + "79853\n", + "79854\n", + "79855\n", + "79856\n", + "79857\n", + "79858\n", + "79859\n", + "79860\n", + "79861\n", + "79862\n", + "79863\n", + "79864\n", + "79865\n", + "79866\n", + "79867\n", + "79868\n", + "79869\n", + "79870\n", + "79871\n", + "79872\n", + "79873\n", + "79874\n", + "79875\n", + "79876\n", + "79877\n", + "79878\n", + "79879\n", + "79880\n", + "79881\n", + "79882\n", + "79883\n", + "79884\n", + "79885\n", + "79886\n", + "79887\n", + "79888\n", + "79889\n", + "79890\n", + "79891\n", + "79892\n", + "79893\n", + "79894\n", + "79895\n", + "79896\n", + "79897\n", + "79898\n", + "79899\n", + "79900\n", + "79901\n", + "79902\n", + "79903\n", + "79904\n", + "79905\n", + "79906\n", + "79907\n", + "79908\n", + "79909\n", + "79910\n", + "79911\n", + "79912\n", + "79913\n", + "79914\n", + "79915\n", + "79916\n", + "79917\n", + "79918\n", + "79919\n", + "79920\n", + "79921\n", + "79922\n", + "79923\n", + "79924\n", + "79925\n", + "79926\n", + "79927\n", + "79928\n", + "79929\n", + "79930\n", + "79931\n", + "79932\n", + "79933\n", + "79934\n", + "79935\n", + "79936\n", + "79937\n", + "79938\n", + "79939\n", + "79940\n", + "79941\n", + "79942\n", + "79943\n", + "79944\n", + "79945\n", + "79946\n", + "79947\n", + "79948\n", + "79949\n", + "79950\n", + "79951\n", + "79952\n", + "79953\n", + "79954\n", + "79955\n", + "79956\n", + "79957\n", + "79958\n", + "79959\n", + "79960\n", + "79961\n", + "79962\n", + "79963\n", + "79964\n", + "79965\n", + "79966\n", + "79967\n", + "79968\n", + "79969\n", + "79970\n", + "79971\n", + "79972\n", + "79973\n", + "79974\n", + "79975\n", + "79976\n", + "79977\n", + "79978\n", + "79979\n", + "79980\n", + "79981\n", + "79982\n", + "79983\n", + "79984\n", + "79985\n", + "79986\n", + "79987\n", + "79988\n", + "79989\n", + "79990\n", + "79991\n", + "79992\n", + "79993\n", + "79994\n", + "79995\n", + "79996\n", + "79997\n", + "79998\n", + "79999\n", + "80000\n", + "80001\n", + "80002\n", + "80003\n", + "80004\n", + "80005\n", + "80006\n", + "80007\n", + "80008\n", + "80009\n", + "80010\n", + "80011\n", + "80012\n", + "80013\n", + "80014\n", + "80015\n", + "80016\n", + "80017\n", + "80018\n", + "80019\n", + "80020\n", + "80021\n", + "80022\n", + "80023\n", + "80024\n", + "80025\n", + "80026\n", + "80027\n", + "80028\n", + "80029\n", + "80030\n", + "80031\n", + "80032\n", + "80033\n", + "80034\n", + "80035\n", + "80036\n", + "80037\n", + "80038\n", + "80039\n", + "80040\n", + "80041\n", + "80042\n", + "80043\n", + "80044\n", + "80045\n", + "80046\n", + "80047\n", + "80048\n", + "80049\n", + "80050\n", + "80051\n", + "80052\n", + "80053\n", + "80054\n", + "80055\n", + "80056\n", + "80057\n", + "80058\n", + "80059\n", + "80060\n", + "80061\n", + "80062\n", + "80063\n", + "80064\n", + "80065\n", + "80066\n", + "80067\n", + "80068\n", + "80069\n", + "80070\n", + "80071\n", + "80072\n", + "80073\n", + "80074\n", + "80075\n", + "80076\n", + "80077\n", + "80078\n", + "80079\n", + "80080\n", + "80081\n", + "80082\n", + "80083\n", + "80084\n", + "80085\n", + "80086\n", + "80087\n", + "80088\n", + "80089\n", + "80090\n", + "80091\n", + "80092\n", + "80093\n", + "80094\n", + "80095\n", + "80096\n", + "80097\n", + "80098\n", + "80099\n", + "80100\n", + "80101\n", + "80102\n", + "80103\n", + "80104\n", + "80105\n", + "80106\n", + "80107\n", + "80108\n", + "80109\n", + "80110\n", + "80111\n", + "80112\n", + "80113\n", + "80114\n", + "80115\n", + "80116\n", + "80117\n", + "80118\n", + "80119\n", + "80120\n", + "80121\n", + "80122\n", + "80123\n", + "80124\n", + "80125\n", + "80126\n", + "80127\n", + "80128\n", + "80129\n", + "80130\n", + "80131\n", + "80132\n", + "80133\n", + "80134\n", + "80135\n", + "80136\n", + "80137\n", + "80138\n", + "80139\n", + "80140\n", + "80141\n", + "80142\n", + "80143\n", + "80144\n", + "80145\n", + "80146\n", + "80147\n", + "80148\n", + "80149\n", + "80150\n", + "80151\n", + "80152\n", + "80153\n", + "80154\n", + "80155\n", + "80156\n", + "80157\n", + "80158\n", + "80159\n", + "80160\n", + "80161\n", + "80162\n", + "80163\n", + "80164\n", + "80165\n", + "80166\n", + "80167\n", + "80168\n", + "80169\n", + "80170\n", + "80171\n", + "80172\n", + "80173\n", + "80174\n", + "80175\n", + "80176\n", + "80177\n", + "80178\n", + "80179\n", + "80180\n", + "80181\n", + "80182\n", + "80183\n", + "80184\n", + "80185\n", + "80186\n", + "80187\n", + "80188\n", + "80189\n", + "80190\n", + "80191\n", + "80192\n", + "80193\n", + "80194\n", + "80195\n", + "80196\n", + "80197\n", + "80198\n", + "80199\n", + "80200\n", + "80201\n", + "80202\n", + "80203\n", + "80204\n", + "80205\n", + "80206\n", + "80207\n", + "80208\n", + "80209\n", + "80210\n", + "80211\n", + "80212\n", + "80213\n", + "80214\n", + "80215\n", + "80216\n", + "80217\n", + "80218\n", + "80219\n", + "80220\n", + "80221\n", + "80222\n", + "80223\n", + "80224\n", + "80225\n", + "80226\n", + "80227\n", + "80228\n", + "80229\n", + "80230\n", + "80231\n", + "80232\n", + "80233\n", + "80234\n", + "80235\n", + "80236\n", + "80237\n", + "80238\n", + "80239\n", + "80240\n", + "80241\n", + "80242\n", + "80243\n", + "80244\n", + "80245\n", + "80246\n", + "80247\n", + "80248\n", + "80249\n", + "80250\n", + "80251\n", + "80252\n", + "80253\n", + "80254\n", + "80255\n", + "80256\n", + "80257\n", + "80258\n", + "80259\n", + "80260\n", + "80261\n", + "80262\n", + "80263\n", + "80264\n", + "80265\n", + "80266\n", + "80267\n", + "80268\n", + "80269\n", + "80270\n", + "80271\n", + "80272\n", + "80273\n", + "80274\n", + "80275\n", + "80276\n", + "80277\n", + "80278\n", + "80279\n", + "80280\n", + "80281\n", + "80282\n", + "80283\n", + "80284\n", + "80285\n", + "80286\n", + "80287\n", + "80288\n", + "80289\n", + "80290\n", + "80291\n", + "80292\n", + "80293\n", + "80294\n", + "80295\n", + "80296\n", + "80297\n", + "80298\n", + "80299\n", + "80300\n", + "80301\n", + "80302\n", + "80303\n", + "80304\n", + "80305\n", + "80306\n", + "80307\n", + "80308\n", + "80309\n", + "80310\n", + "80311\n", + "80312\n", + "80313\n", + "80314\n", + "80315\n", + "80316\n", + "80317\n", + "80318\n", + "80319\n", + "80320\n", + "80321\n", + "80322\n", + "80323\n", + "80324\n", + "80325\n", + "80326\n", + "80327\n", + "80328\n", + "80329\n", + "80330\n", + "80331\n", + "80332\n", + "80333\n", + "80334\n", + "80335\n", + "80336\n", + "80337\n", + "80338\n", + "80339\n", + "80340\n", + "80341\n", + "80342\n", + "80343\n", + "80344\n", + "80345\n", + "80346\n", + "80347\n", + "80348\n", + "80349\n", + "80350\n", + "80351\n", + "80352\n", + "80353\n", + "80354\n", + "80355\n", + "80356\n", + "80357\n", + "80358\n", + "80359\n", + "80360\n", + "80361\n", + "80362\n", + "80363\n", + "80364\n", + "80365\n", + "80366\n", + "80367\n", + "80368\n", + "80369\n", + "80370\n", + "80371\n", + "80372\n", + "80373\n", + "80374\n", + "80375\n", + "80376\n", + "80377\n", + "80378\n", + "80379\n", + "80380\n", + "80381\n", + "80382\n", + "80383\n", + "80384\n", + "80385\n", + "80386\n", + "80387\n", + "80388\n", + "80389\n", + "80390\n", + "80391\n", + "80392\n", + "80393\n", + "80394\n", + "80395\n", + "80396\n", + "80397\n", + "80398\n", + "80399\n", + "80400\n", + "80401\n", + "80402\n", + "80403\n", + "80404\n", + "80405\n", + "80406\n", + "80407\n", + "80408\n", + "80409\n", + "80410\n", + "80411\n", + "80412\n", + "80413\n", + "80414\n", + "80415\n", + "80416\n", + "80417\n", + "80418\n", + "80419\n", + "80420\n", + "80421\n", + "80422\n", + "80423\n", + "80424\n", + "80425\n", + "80426\n", + "80427\n", + "80428\n", + "80429\n", + "80430\n", + "80431\n", + "80432\n", + "80433\n", + "80434\n", + "80435\n", + "80436\n", + "80437\n", + "80438\n", + "80439\n", + "80440\n", + "80441\n", + "80442\n", + "80443\n", + "80444\n", + "80445\n", + "80446\n", + "80447\n", + "80448\n", + "80449\n", + "80450\n", + "80451\n", + "80452\n", + "80453\n", + "80454\n", + "80455\n", + "80456\n", + "80457\n", + "80458\n", + "80459\n", + "80460\n", + "80461\n", + "80462\n", + "80463\n", + "80464\n", + "80465\n", + "80466\n", + "80467\n", + "80468\n", + "80469\n", + "80470\n", + "80471\n", + "80472\n", + "80473\n", + "80474\n", + "80475\n", + "80476\n", + "80477\n", + "80478\n", + "80479\n", + "80480\n", + "80481\n", + "80482\n", + "80483\n", + "80484\n", + "80485\n", + "80486\n", + "80487\n", + "80488\n", + "80489\n", + "80490\n", + "80491\n", + "80492\n", + "80493\n", + "80494\n", + "80495\n", + "80496\n", + "80497\n", + "80498\n", + "80499\n", + "80500\n", + "80501\n", + "80502\n", + "80503\n", + "80504\n", + "80505\n", + "80506\n", + "80507\n", + "80508\n", + "80509\n", + "80510\n", + "80511\n", + "80512\n", + "80513\n", + "80514\n", + "80515\n", + "80516\n", + "80517\n", + "80518\n", + "80519\n", + "80520\n", + "80521\n", + "80522\n", + "80523\n", + "80524\n", + "80525\n", + "80526\n", + "80527\n", + "80528\n", + "80529\n", + "80530\n", + "80531\n", + "80532\n", + "80533\n", + "80534\n", + "80535\n", + "80536\n", + "80537\n", + "80538\n", + "80539\n", + "80540\n", + "80541\n", + "80542\n", + "80543\n", + "80544\n", + "80545\n", + "80546\n", + "80547\n", + "80548\n", + "80549\n", + "80550\n", + "80551\n", + "80552\n", + "80553\n", + "80554\n", + "80555\n", + "80556\n", + "80557\n", + "80558\n", + "80559\n", + "80560\n", + "80561\n", + "80562\n", + "80563\n", + "80564\n", + "80565\n", + "80566\n", + "80567\n", + "80568\n", + "80569\n", + "80570\n", + "80571\n", + "80572\n", + "80573\n", + "80574\n", + "80575\n", + "80576\n", + "80577\n", + "80578\n", + "80579\n", + "80580\n", + "80581\n", + "80582\n", + "80583\n", + "80584\n", + "80585\n", + "80586\n", + "80587\n", + "80588\n", + "80589\n", + "80590\n", + "80591\n", + "80592\n", + "80593\n", + "80594\n", + "80595\n", + "80596\n", + "80597\n", + "80598\n", + "80599\n", + "80600\n", + "80601\n", + "80602\n", + "80603\n", + "80604\n", + "80605\n", + "80606\n", + "80607\n", + "80608\n", + "80609\n", + "80610\n", + "80611\n", + "80612\n", + "80613\n", + "80614\n", + "80615\n", + "80616\n", + "80617\n", + "80618\n", + "80619\n", + "80620\n", + "80621\n", + "80622\n", + "80623\n", + "80624\n", + "80625\n", + "80626\n", + "80627\n", + "80628\n", + "80629\n", + "80630\n", + "80631\n", + "80632\n", + "80633\n", + "80634\n", + "80635\n", + "80636\n", + "80637\n", + "80638\n", + "80639\n", + "80640\n", + "80641\n", + "80642\n", + "80643\n", + "80644\n", + "80645\n", + "80646\n", + "80647\n", + "80648\n", + "80649\n", + "80650\n", + "80651\n", + "80652\n", + "80653\n", + "80654\n", + "80655\n", + "80656\n", + "80657\n", + "80658\n", + "80659\n", + "80660\n", + "80661\n", + "80662\n", + "80663\n", + "80664\n", + "80665\n", + "80666\n", + "80667\n", + "80668\n", + "80669\n", + "80670\n", + "80671\n", + "80672\n", + "80673\n", + "80674\n", + "80675\n", + "80676\n", + "80677\n", + "80678\n", + "80679\n", + "80680\n", + "80681\n", + "80682\n", + "80683\n", + "80684\n", + "80685\n", + "80686\n", + "80687\n", + "80688\n", + "80689\n", + "80690\n", + "80691\n", + "80692\n", + "80693\n", + "80694\n", + "80695\n", + "80696\n", + "80697\n", + "80698\n", + "80699\n", + "80700\n", + "80701\n", + "80702\n", + "80703\n", + "80704\n", + "80705\n", + "80706\n", + "80707\n", + "80708\n", + "80709\n", + "80710\n", + "80711\n", + "80712\n", + "80713\n", + "80714\n", + "80715\n", + "80716\n", + "80717\n", + "80718\n", + "80719\n", + "80720\n", + "80721\n", + "80722\n", + "80723\n", + "80724\n", + "80725\n", + "80726\n", + "80727\n", + "80728\n", + "80729\n", + "80730\n", + "80731\n", + "80732\n", + "80733\n", + "80734\n", + "80735\n", + "80736\n", + "80737\n", + "80738\n", + "80739\n", + "80740\n", + "80741\n", + "80742\n", + "80743\n", + "80744\n", + "80745\n", + "80746\n", + "80747\n", + "80748\n", + "80749\n", + "80750\n", + "80751\n", + "80752\n", + "80753\n", + "80754\n", + "80755\n", + "80756\n", + "80757\n", + "80758\n", + "80759\n", + "80760\n", + "80761\n", + "80762\n", + "80763\n", + "80764\n", + "80765\n", + "80766\n", + "80767\n", + "80768\n", + "80769\n", + "80770\n", + "80771\n", + "80772\n", + "80773\n", + "80774\n", + "80775\n", + "80776\n", + "80777\n", + "80778\n", + "80779\n", + "80780\n", + "80781\n", + "80782\n", + "80783\n", + "80784\n", + "80785\n", + "80786\n", + "80787\n", + "80788\n", + "80789\n", + "80790\n", + "80791\n", + "80792\n", + "80793\n", + "80794\n", + "80795\n", + "80796\n", + "80797\n", + "80798\n", + "80799\n", + "80800\n", + "80801\n", + "80802\n", + "80803\n", + "80804\n", + "80805\n", + "80806\n", + "80807\n", + "80808\n", + "80809\n", + "80810\n", + "80811\n", + "80812\n", + "80813\n", + "80814\n", + "80815\n", + "80816\n", + "80817\n", + "80818\n", + "80819\n", + "80820\n", + "80821\n", + "80822\n", + "80823\n", + "80824\n", + "80825\n", + "80826\n", + "80827\n", + "80828\n", + "80829\n", + "80830\n", + "80831\n", + "80832\n", + "80833\n", + "80834\n", + "80835\n", + "80836\n", + "80837\n", + "80838\n", + "80839\n", + "80840\n", + "80841\n", + "80842\n", + "80843\n", + "80844\n", + "80845\n", + "80846\n", + "80847\n", + "80848\n", + "80849\n", + "80850\n", + "80851\n", + "80852\n", + "80853\n", + "80854\n", + "80855\n", + "80856\n", + "80857\n", + "80858\n", + "80859\n", + "80860\n", + "80861\n", + "80862\n", + "80863\n", + "80864\n", + "80865\n", + "80866\n", + "80867\n", + "80868\n", + "80869\n", + "80870\n", + "80871\n", + "80872\n", + "80873\n", + "80874\n", + "80875\n", + "80876\n", + "80877\n", + "80878\n", + "80879\n", + "80880\n", + "80881\n", + "80882\n", + "80883\n", + "80884\n", + "80885\n", + "80886\n", + "80887\n", + "80888\n", + "80889\n", + "80890\n", + "80891\n", + "80892\n", + "80893\n", + "80894\n", + "80895\n", + "80896\n", + "80897\n", + "80898\n", + "80899\n", + "80900\n", + "80901\n", + "80902\n", + "80903\n", + "80904\n", + "80905\n", + "80906\n", + "80907\n", + "80908\n", + "80909\n", + "80910\n", + "80911\n", + "80912\n", + "80913\n", + "80914\n", + "80915\n", + "80916\n", + "80917\n", + "80918\n", + "80919\n", + "80920\n", + "80921\n", + "80922\n", + "80923\n", + "80924\n", + "80925\n", + "80926\n", + "80927\n", + "80928\n", + "80929\n", + "80930\n", + "80931\n", + "80932\n", + "80933\n", + "80934\n", + "80935\n", + "80936\n", + "80937\n", + "80938\n", + "80939\n", + "80940\n", + "80941\n", + "80942\n", + "80943\n", + "80944\n", + "80945\n", + "80946\n", + "80947\n", + "80948\n", + "80949\n", + "80950\n", + "80951\n", + "80952\n", + "80953\n", + "80954\n", + "80955\n", + "80956\n", + "80957\n", + "80958\n", + "80959\n", + "80960\n", + "80961\n", + "80962\n", + "80963\n", + "80964\n", + "80965\n", + "80966\n", + "80967\n", + "80968\n", + "80969\n", + "80970\n", + "80971\n", + "80972\n", + "80973\n", + "80974\n", + "80975\n", + "80976\n", + "80977\n", + "80978\n", + "80979\n", + "80980\n", + "80981\n", + "80982\n", + "80983\n", + "80984\n", + "80985\n", + "80986\n", + "80987\n", + "80988\n", + "80989\n", + "80990\n", + "80991\n", + "80992\n", + "80993\n", + "80994\n", + "80995\n", + "80996\n", + "80997\n", + "80998\n", + "80999\n", + "81000\n", + "81001\n", + "81002\n", + "81003\n", + "81004\n", + "81005\n", + "81006\n", + "81007\n", + "81008\n", + "81009\n", + "81010\n", + "81011\n", + "81012\n", + "81013\n", + "81014\n", + "81015\n", + "81016\n", + "81017\n", + "81018\n", + "81019\n", + "81020\n", + "81021\n", + "81022\n", + "81023\n", + "81024\n", + "81025\n", + "81026\n", + "81027\n", + "81028\n", + "81029\n", + "81030\n", + "81031\n", + "81032\n", + "81033\n", + "81034\n", + "81035\n", + "81036\n", + "81037\n", + "81038\n", + "81039\n", + "81040\n", + "81041\n", + "81042\n", + "81043\n", + "81044\n", + "81045\n", + "81046\n", + "81047\n", + "81048\n", + "81049\n", + "81050\n", + "81051\n", + "81052\n", + "81053\n", + "81054\n", + "81055\n", + "81056\n", + "81057\n", + "81058\n", + "81059\n", + "81060\n", + "81061\n", + "81062\n", + "81063\n", + "81064\n", + "81065\n", + "81066\n", + "81067\n", + "81068\n", + "81069\n", + "81070\n", + "81071\n", + "81072\n", + "81073\n", + "81074\n", + "81075\n", + "81076\n", + "81077\n", + "81078\n", + "81079\n", + "81080\n", + "81081\n", + "81082\n", + "81083\n", + "81084\n", + "81085\n", + "81086\n", + "81087\n", + "81088\n", + "81089\n", + "81090\n", + "81091\n", + "81092\n", + "81093\n", + "81094\n", + "81095\n", + "81096\n", + "81097\n", + "81098\n", + "81099\n", + "81100\n", + "81101\n", + "81102\n", + "81103\n", + "81104\n", + "81105\n", + "81106\n", + "81107\n", + "81108\n", + "81109\n", + "81110\n", + "81111\n", + "81112\n", + "81113\n", + "81114\n", + "81115\n", + "81116\n", + "81117\n", + "81118\n", + "81119\n", + "81120\n", + "81121\n", + "81122\n", + "81123\n", + "81124\n", + "81125\n", + "81126\n", + "81127\n", + "81128\n", + "81129\n", + "81130\n", + "81131\n", + "81132\n", + "81133\n", + "81134\n", + "81135\n", + "81136\n", + "81137\n", + "81138\n", + "81139\n", + "81140\n", + "81141\n", + "81142\n", + "81143\n", + "81144\n", + "81145\n", + "81146\n", + "81147\n", + "81148\n", + "81149\n", + "81150\n", + "81151\n", + "81152\n", + "81153\n", + "81154\n", + "81155\n", + "81156\n", + "81157\n", + "81158\n", + "81159\n", + "81160\n", + "81161\n", + "81162\n", + "81163\n", + "81164\n", + "81165\n", + "81166\n", + "81167\n", + "81168\n", + "81169\n", + "81170\n", + "81171\n", + "81172\n", + "81173\n", + "81174\n", + "81175\n", + "81176\n", + "81177\n", + "81178\n", + "81179\n", + "81180\n", + "81181\n", + "81182\n", + "81183\n", + "81184\n", + "81185\n", + "81186\n", + "81187\n", + "81188\n", + "81189\n", + "81190\n", + "81191\n", + "81192\n", + "81193\n", + "81194\n", + "81195\n", + "81196\n", + "81197\n", + "81198\n", + "81199\n", + "81200\n", + "81201\n", + "81202\n", + "81203\n", + "81204\n", + "81205\n", + "81206\n", + "81207\n", + "81208\n", + "81209\n", + "81210\n", + "81211\n", + "81212\n", + "81213\n", + "81214\n", + "81215\n", + "81216\n", + "81217\n", + "81218\n", + "81219\n", + "81220\n", + "81221\n", + "81222\n", + "81223\n", + "81224\n", + "81225\n", + "81226\n", + "81227\n", + "81228\n", + "81229\n", + "81230\n", + "81231\n", + "81232\n", + "81233\n", + "81234\n", + "81235\n", + "81236\n", + "81237\n", + "81238\n", + "81239\n", + "81240\n", + "81241\n", + "81242\n", + "81243\n", + "81244\n", + "81245\n", + "81246\n", + "81247\n", + "81248\n", + "81249\n", + "81250\n", + "81251\n", + "81252\n", + "81253\n", + "81254\n", + "81255\n", + "81256\n", + "81257\n", + "81258\n", + "81259\n", + "81260\n", + "81261\n", + "81262\n", + "81263\n", + "81264\n", + "81265\n", + "81266\n", + "81267\n", + "81268\n", + "81269\n", + "81270\n", + "81271\n", + "81272\n", + "81273\n", + "81274\n", + "81275\n", + "81276\n", + "81277\n", + "81278\n", + "81279\n", + "81280\n", + "81281\n", + "81282\n", + "81283\n", + "81284\n", + "81285\n", + "81286\n", + "81287\n", + "81288\n", + "81289\n", + "81290\n", + "81291\n", + "81292\n", + "81293\n", + "81294\n", + "81295\n", + "81296\n", + "81297\n", + "81298\n", + "81299\n", + "81300\n", + "81301\n", + "81302\n", + "81303\n", + "81304\n", + "81305\n", + "81306\n", + "81307\n", + "81308\n", + "81309\n", + "81310\n", + "81311\n", + "81312\n", + "81313\n", + "81314\n", + "81315\n", + "81316\n", + "81317\n", + "81318\n", + "81319\n", + "81320\n", + "81321\n", + "81322\n", + "81323\n", + "81324\n", + "81325\n", + "81326\n", + "81327\n", + "81328\n", + "81329\n", + "81330\n", + "81331\n", + "81332\n", + "81333\n", + "81334\n", + "81335\n", + "81336\n", + "81337\n", + "81338\n", + "81339\n", + "81340\n", + "81341\n", + "81342\n", + "81343\n", + "81344\n", + "81345\n", + "81346\n", + "81347\n", + "81348\n", + "81349\n", + "81350\n", + "81351\n", + "81352\n", + "81353\n", + "81354\n", + "81355\n", + "81356\n", + "81357\n", + "81358\n", + "81359\n", + "81360\n", + "81361\n", + "81362\n", + "81363\n", + "81364\n", + "81365\n", + "81366\n", + "81367\n", + "81368\n", + "81369\n", + "81370\n", + "81371\n", + "81372\n", + "81373\n", + "81374\n", + "81375\n", + "81376\n", + "81377\n", + "81378\n", + "81379\n", + "81380\n", + "81381\n", + "81382\n", + "81383\n", + "81384\n", + "81385\n", + "81386\n", + "81387\n", + "81388\n", + "81389\n", + "81390\n", + "81391\n", + "81392\n", + "81393\n", + "81394\n", + "81395\n", + "81396\n", + "81397\n", + "81398\n", + "81399\n", + "81400\n", + "81401\n", + "81402\n", + "81403\n", + "81404\n", + "81405\n", + "81406\n", + "81407\n", + "81408\n", + "81409\n", + "81410\n", + "81411\n", + "81412\n", + "81413\n", + "81414\n", + "81415\n", + "81416\n", + "81417\n", + "81418\n", + "81419\n", + "81420\n", + "81421\n", + "81422\n", + "81423\n", + "81424\n", + "81425\n", + "81426\n", + "81427\n", + "81428\n", + "81429\n", + "81430\n", + "81431\n", + "81432\n", + "81433\n", + "81434\n", + "81435\n", + "81436\n", + "81437\n", + "81438\n", + "81439\n", + "81440\n", + "81441\n", + "81442\n", + "81443\n", + "81444\n", + "81445\n", + "81446\n", + "81447\n", + "81448\n", + "81449\n", + "81450\n", + "81451\n", + "81452\n", + "81453\n", + "81454\n", + "81455\n", + "81456\n", + "81457\n", + "81458\n", + "81459\n", + "81460\n", + "81461\n", + "81462\n", + "81463\n", + "81464\n", + "81465\n", + "81466\n", + "81467\n", + "81468\n", + "81469\n", + "81470\n", + "81471\n", + "81472\n", + "81473\n", + "81474\n", + "81475\n", + "81476\n", + "81477\n", + "81478\n", + "81479\n", + "81480\n", + "81481\n", + "81482\n", + "81483\n", + "81484\n", + "81485\n", + "81486\n", + "81487\n", + "81488\n", + "81489\n", + "81490\n", + "81491\n", + "81492\n", + "81493\n", + "81494\n", + "81495\n", + "81496\n", + "81497\n", + "81498\n", + "81499\n", + "81500\n", + "81501\n", + "81502\n", + "81503\n", + "81504\n", + "81505\n", + "81506\n", + "81507\n", + "81508\n", + "81509\n", + "81510\n", + "81511\n", + "81512\n", + "81513\n", + "81514\n", + "81515\n", + "81516\n", + "81517\n", + "81518\n", + "81519\n", + "81520\n", + "81521\n", + "81522\n", + "81523\n", + "81524\n", + "81525\n", + "81526\n", + "81527\n", + "81528\n", + "81529\n", + "81530\n", + "81531\n", + "81532\n", + "81533\n", + "81534\n", + "81535\n", + "81536\n", + "81537\n", + "81538\n", + "81539\n", + "81540\n", + "81541\n", + "81542\n", + "81543\n", + "81544\n", + "81545\n", + "81546\n", + "81547\n", + "81548\n", + "81549\n", + "81550\n", + "81551\n", + "81552\n", + "81553\n", + "81554\n", + "81555\n", + "81556\n", + "81557\n", + "81558\n", + "81559\n", + "81560\n", + "81561\n", + "81562\n", + "81563\n", + "81564\n", + "81565\n", + "81566\n", + "81567\n", + "81568\n", + "81569\n", + "81570\n", + "81571\n", + "81572\n", + "81573\n", + "81574\n", + "81575\n", + "81576\n", + "81577\n", + "81578\n", + "81579\n", + "81580\n", + "81581\n", + "81582\n", + "81583\n", + "81584\n", + "81585\n", + "81586\n", + "81587\n", + "81588\n", + "81589\n", + "81590\n", + "81591\n", + "81592\n", + "81593\n", + "81594\n", + "81595\n", + "81596\n", + "81597\n", + "81598\n", + "81599\n", + "81600\n", + "81601\n", + "81602\n", + "81603\n", + "81604\n", + "81605\n", + "81606\n", + "81607\n", + "81608\n", + "81609\n", + "81610\n", + "81611\n", + "81612\n", + "81613\n", + "81614\n", + "81615\n", + "81616\n", + "81617\n", + "81618\n", + "81619\n", + "81620\n", + "81621\n", + "81622\n", + "81623\n", + "81624\n", + "81625\n", + "81626\n", + "81627\n", + "81628\n", + "81629\n", + "81630\n", + "81631\n", + "81632\n", + "81633\n", + "81634\n", + "81635\n", + "81636\n", + "81637\n", + "81638\n", + "81639\n", + "81640\n", + "81641\n", + "81642\n", + "81643\n", + "81644\n", + "81645\n", + "81646\n", + "81647\n", + "81648\n", + "81649\n", + "81650\n", + "81651\n", + "81652\n", + "81653\n", + "81654\n", + "81655\n", + "81656\n", + "81657\n", + "81658\n", + "81659\n", + "81660\n", + "81661\n", + "81662\n", + "81663\n", + "81664\n", + "81665\n", + "81666\n", + "81667\n", + "81668\n", + "81669\n", + "81670\n", + "81671\n", + "81672\n", + "81673\n", + "81674\n", + "81675\n", + "81676\n", + "81677\n", + "81678\n", + "81679\n", + "81680\n", + "81681\n", + "81682\n", + "81683\n", + "81684\n", + "81685\n", + "81686\n", + "81687\n", + "81688\n", + "81689\n", + "81690\n", + "81691\n", + "81692\n", + "81693\n", + "81694\n", + "81695\n", + "81696\n", + "81697\n", + "81698\n", + "81699\n", + "81700\n", + "81701\n", + "81702\n", + "81703\n", + "81704\n", + "81705\n", + "81706\n", + "81707\n", + "81708\n", + "81709\n", + "81710\n", + "81711\n", + "81712\n", + "81713\n", + "81714\n", + "81715\n", + "81716\n", + "81717\n", + "81718\n", + "81719\n", + "81720\n", + "81721\n", + "81722\n", + "81723\n", + "81724\n", + "81725\n", + "81726\n", + "81727\n", + "81728\n", + "81729\n", + "81730\n", + "81731\n", + "81732\n", + "81733\n", + "81734\n", + "81735\n", + "81736\n", + "81737\n", + "81738\n", + "81739\n", + "81740\n", + "81741\n", + "81742\n", + "81743\n", + "81744\n", + "81745\n", + "81746\n", + "81747\n", + "81748\n", + "81749\n", + "81750\n", + "81751\n", + "81752\n", + "81753\n", + "81754\n", + "81755\n", + "81756\n", + "81757\n", + "81758\n", + "81759\n", + "81760\n", + "81761\n", + "81762\n", + "81763\n", + "81764\n", + "81765\n", + "81766\n", + "81767\n", + "81768\n", + "81769\n", + "81770\n", + "81771\n", + "81772\n", + "81773\n", + "81774\n", + "81775\n", + "81776\n", + "81777\n", + "81778\n", + "81779\n", + "81780\n", + "81781\n", + "81782\n", + "81783\n", + "81784\n", + "81785\n", + "81786\n", + "81787\n", + "81788\n", + "81789\n", + "81790\n", + "81791\n", + "81792\n", + "81793\n", + "81794\n", + "81795\n", + "81796\n", + "81797\n", + "81798\n", + "81799\n", + "81800\n", + "81801\n", + "81802\n", + "81803\n", + "81804\n", + "81805\n", + "81806\n", + "81807\n", + "81808\n", + "81809\n", + "81810\n", + "81811\n", + "81812\n", + "81813\n", + "81814\n", + "81815\n", + "81816\n", + "81817\n", + "81818\n", + "81819\n", + "81820\n", + "81821\n", + "81822\n", + "81823\n", + "81824\n", + "81825\n", + "81826\n", + "81827\n", + "81828\n", + "81829\n", + "81830\n", + "81831\n", + "81832\n", + "81833\n", + "81834\n", + "81835\n", + "81836\n", + "81837\n", + "81838\n", + "81839\n", + "81840\n", + "81841\n", + "81842\n", + "81843\n", + "81844\n", + "81845\n", + "81846\n", + "81847\n", + "81848\n", + "81849\n", + "81850\n", + "81851\n", + "81852\n", + "81853\n", + "81854\n", + "81855\n", + "81856\n", + "81857\n", + "81858\n", + "81859\n", + "81860\n", + "81861\n", + "81862\n", + "81863\n", + "81864\n", + "81865\n", + "81866\n", + "81867\n", + "81868\n", + "81869\n", + "81870\n", + "81871\n", + "81872\n", + "81873\n", + "81874\n", + "81875\n", + "81876\n", + "81877\n", + "81878\n", + "81879\n", + "81880\n", + "81881\n", + "81882\n", + "81883\n", + "81884\n", + "81885\n", + "81886\n", + "81887\n", + "81888\n", + "81889\n", + "81890\n", + "81891\n", + "81892\n", + "81893\n", + "81894\n", + "81895\n", + "81896\n", + "81897\n", + "81898\n", + "81899\n", + "81900\n", + "81901\n", + "81902\n", + "81903\n", + "81904\n", + "81905\n", + "81906\n", + "81907\n", + "81908\n", + "81909\n", + "81910\n", + "81911\n", + "81912\n", + "81913\n", + "81914\n", + "81915\n", + "81916\n", + "81917\n", + "81918\n", + "81919\n", + "81920\n", + "81921\n", + "81922\n", + "81923\n", + "81924\n", + "81925\n", + "81926\n", + "81927\n", + "81928\n", + "81929\n", + "81930\n", + "81931\n", + "81932\n", + "81933\n", + "81934\n", + "81935\n", + "81936\n", + "81937\n", + "81938\n", + "81939\n", + "81940\n", + "81941\n", + "81942\n", + "81943\n", + "81944\n", + "81945\n", + "81946\n", + "81947\n", + "81948\n", + "81949\n", + "81950\n", + "81951\n", + "81952\n", + "81953\n", + "81954\n", + "81955\n", + "81956\n", + "81957\n", + "81958\n", + "81959\n", + "81960\n", + "81961\n", + "81962\n", + "81963\n", + "81964\n", + "81965\n", + "81966\n", + "81967\n", + "81968\n", + "81969\n", + "81970\n", + "81971\n", + "81972\n", + "81973\n", + "81974\n", + "81975\n", + "81976\n", + "81977\n", + "81978\n", + "81979\n", + "81980\n", + "81981\n", + "81982\n", + "81983\n", + "81984\n", + "81985\n", + "81986\n", + "81987\n", + "81988\n", + "81989\n", + "81990\n", + "81991\n", + "81992\n", + "81993\n", + "81994\n", + "81995\n", + "81996\n", + "81997\n", + "81998\n", + "81999\n", + "82000\n", + "82001\n", + "82002\n", + "82003\n", + "82004\n", + "82005\n", + "82006\n", + "82007\n", + "82008\n", + "82009\n", + "82010\n", + "82011\n", + "82012\n", + "82013\n", + "82014\n", + "82015\n", + "82016\n", + "82017\n", + "82018\n", + "82019\n", + "82020\n", + "82021\n", + "82022\n", + "82023\n", + "82024\n", + "82025\n", + "82026\n", + "82027\n", + "82028\n", + "82029\n", + "82030\n", + "82031\n", + "82032\n", + "82033\n", + "82034\n", + "82035\n", + "82036\n", + "82037\n", + "82038\n", + "82039\n", + "82040\n", + "82041\n", + "82042\n", + "82043\n", + "82044\n", + "82045\n", + "82046\n", + "82047\n", + "82048\n", + "82049\n", + "82050\n", + "82051\n", + "82052\n", + "82053\n", + "82054\n", + "82055\n", + "82056\n", + "82057\n", + "82058\n", + "82059\n", + "82060\n", + "82061\n", + "82062\n", + "82063\n", + "82064\n", + "82065\n", + "82066\n", + "82067\n", + "82068\n", + "82069\n", + "82070\n", + "82071\n", + "82072\n", + "82073\n", + "82074\n", + "82075\n", + "82076\n", + "82077\n", + "82078\n", + "82079\n", + "82080\n", + "82081\n", + "82082\n", + "82083\n", + "82084\n", + "82085\n", + "82086\n", + "82087\n", + "82088\n", + "82089\n", + "82090\n", + "82091\n", + "82092\n", + "82093\n", + "82094\n", + "82095\n", + "82096\n", + "82097\n", + "82098\n", + "82099\n", + "82100\n", + "82101\n", + "82102\n", + "82103\n", + "82104\n", + "82105\n", + "82106\n", + "82107\n", + "82108\n", + "82109\n", + "82110\n", + "82111\n", + "82112\n", + "82113\n", + "82114\n", + "82115\n", + "82116\n", + "82117\n", + "82118\n", + "82119\n", + "82120\n", + "82121\n", + "82122\n", + "82123\n", + "82124\n", + "82125\n", + "82126\n", + "82127\n", + "82128\n", + "82129\n", + "82130\n", + "82131\n", + "82132\n", + "82133\n", + "82134\n", + "82135\n", + "82136\n", + "82137\n", + "82138\n", + "82139\n", + "82140\n", + "82141\n", + "82142\n", + "82143\n", + "82144\n", + "82145\n", + "82146\n", + "82147\n", + "82148\n", + "82149\n", + "82150\n", + "82151\n", + "82152\n", + "82153\n", + "82154\n", + "82155\n", + "82156\n", + "82157\n", + "82158\n", + "82159\n", + "82160\n", + "82161\n", + "82162\n", + "82163\n", + "82164\n", + "82165\n", + "82166\n", + "82167\n", + "82168\n", + "82169\n", + "82170\n", + "82171\n", + "82172\n", + "82173\n", + "82174\n", + "82175\n", + "82176\n", + "82177\n", + "82178\n", + "82179\n", + "82180\n", + "82181\n", + "82182\n", + "82183\n", + "82184\n", + "82185\n", + "82186\n", + "82187\n", + "82188\n", + "82189\n", + "82190\n", + "82191\n", + "82192\n", + "82193\n", + "82194\n", + "82195\n", + "82196\n", + "82197\n", + "82198\n", + "82199\n", + "82200\n", + "82201\n", + "82202\n", + "82203\n", + "82204\n", + "82205\n", + "82206\n", + "82207\n", + "82208\n", + "82209\n", + "82210\n", + "82211\n", + "82212\n", + "82213\n", + "82214\n", + "82215\n", + "82216\n", + "82217\n", + "82218\n", + "82219\n", + "82220\n", + "82221\n", + "82222\n", + "82223\n", + "82224\n", + "82225\n", + "82226\n", + "82227\n", + "82228\n", + "82229\n", + "82230\n", + "82231\n", + "82232\n", + "82233\n", + "82234\n", + "82235\n", + "82236\n", + "82237\n", + "82238\n", + "82239\n", + "82240\n", + "82241\n", + "82242\n", + "82243\n", + "82244\n", + "82245\n", + "82246\n", + "82247\n", + "82248\n", + "82249\n", + "82250\n", + "82251\n", + "82252\n", + "82253\n", + "82254\n", + "82255\n", + "82256\n", + "82257\n", + "82258\n", + "82259\n", + "82260\n", + "82261\n", + "82262\n", + "82263\n", + "82264\n", + "82265\n", + "82266\n", + "82267\n", + "82268\n", + "82269\n", + "82270\n", + "82271\n", + "82272\n", + "82273\n", + "82274\n", + "82275\n", + "82276\n", + "82277\n", + "82278\n", + "82279\n", + "82280\n", + "82281\n", + "82282\n", + "82283\n", + "82284\n", + "82285\n", + "82286\n", + "82287\n", + "82288\n", + "82289\n", + "82290\n", + "82291\n", + "82292\n", + "82293\n", + "82294\n", + "82295\n", + "82296\n", + "82297\n", + "82298\n", + "82299\n", + "82300\n", + "82301\n", + "82302\n", + "82303\n", + "82304\n", + "82305\n", + "82306\n", + "82307\n", + "82308\n", + "82309\n", + "82310\n", + "82311\n", + "82312\n", + "82313\n", + "82314\n", + "82315\n", + "82316\n", + "82317\n", + "82318\n", + "82319\n", + "82320\n", + "82321\n", + "82322\n", + "82323\n", + "82324\n", + "82325\n", + "82326\n", + "82327\n", + "82328\n", + "82329\n", + "82330\n", + "82331\n", + "82332\n", + "82333\n", + "82334\n", + "82335\n", + "82336\n", + "82337\n", + "82338\n", + "82339\n", + "82340\n", + "82341\n", + "82342\n", + "82343\n", + "82344\n", + "82345\n", + "82346\n", + "82347\n", + "82348\n", + "82349\n", + "82350\n", + "82351\n", + "82352\n", + "82353\n", + "82354\n", + "82355\n", + "82356\n", + "82357\n", + "82358\n", + "82359\n", + "82360\n", + "82361\n", + "82362\n", + "82363\n", + "82364\n", + "82365\n", + "82366\n", + "82367\n", + "82368\n", + "82369\n", + "82370\n", + "82371\n", + "82372\n", + "82373\n", + "82374\n", + "82375\n", + "82376\n", + "82377\n", + "82378\n", + "82379\n", + "82380\n", + "82381\n", + "82382\n", + "82383\n", + "82384\n", + "82385\n", + "82386\n", + "82387\n", + "82388\n", + "82389\n", + "82390\n", + "82391\n", + "82392\n", + "82393\n", + "82394\n", + "82395\n", + "82396\n", + "82397\n", + "82398\n", + "82399\n", + "82400\n", + "82401\n", + "82402\n", + "82403\n", + "82404\n", + "82405\n", + "82406\n", + "82407\n", + "82408\n", + "82409\n", + "82410\n", + "82411\n", + "82412\n" + ] + } + ], + "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": { + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
tweet_authorNumero de trendingstrends_max
0100_bolsonaro00
113valber100
21976Mnc00
3ACamargo24100
4AControld33
............
830wolfjorge20100
831yoshio_carlos00
832zemariasccp100
833zeplu100
834zfabrogmailcom00
\n", + "

835 rows × 3 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " tweet_author Numero de trendings trends_max\n", + "0 100_bolsonaro 0 0\n", + "1 13valber1 0 0\n", + "2 1976Mnc 0 0\n", + "3 ACamargo241 0 0\n", + "4 AControld 3 3\n", + ".. ... ... ...\n", + "830 wolfjorge201 0 0\n", + "831 yoshio_carlos 0 0\n", + "832 zemariasccp1 0 0\n", + "833 zeplu1 0 0\n", + "834 zfabrogmailcom 0 0\n", + "\n", + "[835 rows x 3 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 478 + }, + "id": "03RGOP9PvEn4", + "outputId": "7682c941-0b76-44c4-bdf3-44278402cc46" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\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 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? 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 Tempo mediano \\\n", + "0 não NaN lemathes 1917 \n", + "1 não NaN Maurcio98905595 22 \n", + "2 não NaN LunViana 34 \n", + "3 não NaN felipeleixas 40791 \n", + "4 não NaN JoseCar41451194 584 \n", + "\n", + " Tempo menor \n", + "0 16 \n", + "1 1 \n", + "2 2 \n", + "3 141 \n", + "4 9 " + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_handles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 565 + }, + "id": "RxGxu0y1vEn5", + "outputId": "fc9cdd56-2036-4857-ead9-890239308e4c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "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! 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": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 681 + }, + "id": "_TKLTrgQvEn5", + "outputId": "b0f57dad-d4ed-49a3-d608-36455eef543a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "

5 rows × 46 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "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]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817 + }, + "id": "XRayUSq0vEn5", + "outputId": "86c22507-d2bb-4dfc-8fb5-f365afd651cc" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: 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" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "

834 rows × 52 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " Unnamed: 0_x Unnamed: 0.1 tabelaAmostra \\\n", + "0 0 1 https://twitter.com/@lemathes \n", + "1 1 2 https://twitter.com/@Maurcio98905595 \n", + "2 2 3 https://twitter.com/@LunViana \n", + "3 3 4 https://twitter.com/@felipeleixas \n", + "4 4 5 https://twitter.com/@JoseCar41451194 \n", + ".. ... ... ... \n", + "829 1066 1067 https://twitter.com/@CesarNi85939384 \n", + "830 1068 1069 https://twitter.com/@PauloRo49195361 \n", + "831 1070 1071 https://twitter.com/@Marina92011959 \n", + "832 1071 1072 https://twitter.com/@Marcos_28_11_66 \n", + "833 1073 1074 https://twitter.com/@FATIMAC75843178 \n", + "\n", + " p É Bot? 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 - 0 | 0.0%\n", + "12 - 1 | 0.1199040767386091%\n", + "29 - 2 | 0.2398081534772182%\n", + "9 - 3 | 0.3597122302158274%\n", + "29 - 4 | 0.4796163069544364%\n", + "23 - 5 | 0.5995203836930456%\n", + "2 - 6 | 0.7194244604316548%\n", + "21 - 7 | 0.8393285371702638%\n", + "12 - 8 | 0.9592326139088728%\n", + "19 - 9 | 1.079136690647482%\n", + "17 - 10 | 1.1990407673860912%\n", + "44 - 11 | 1.3189448441247003%\n", + "22 - 12 | 1.4388489208633095%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: 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", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 - 13 | 1.5587529976019185%\n", + "26 - 14 | 1.6786570743405276%\n", + "22 - 15 | 1.7985611510791366%\n", + "29 - 16 | 1.9184652278177456%\n", + "17 - 17 | 2.038369304556355%\n", + "19 - 18 | 2.158273381294964%\n", + "16 - 19 | 2.278177458033573%\n", + "30 - 20 | 2.3980815347721824%\n", + "0 - 21 | 2.5179856115107913%\n", + "4 - 22 | 2.6378896882494005%\n", + "26 - 23 | 2.7577937649880093%\n", + "16 - 24 | 2.877697841726619%\n", + "24 - 25 | 2.997601918465228%\n", + "10 - 26 | 3.117505995203837%\n", + "7 - 27 | 3.237410071942446%\n", + "34 - 28 | 3.357314148681055%\n", + "11 - 29 | 3.477218225419664%\n", + "11 - 30 | 3.597122302158273%\n", + "15 - 31 | 3.7170263788968825%\n", + "3 - 32 | 3.8369304556354913%\n", + "35 - 33 | 3.9568345323741005%\n", + "18 - 34 | 4.07673860911271%\n", + "6 - 35 | 4.1966426858513195%\n", + "17 - 36 | 4.316546762589928%\n", + "18 - 37 | 4.436450839328537%\n", + "24 - 38 | 4.556354916067146%\n", + "15 - 39 | 4.676258992805756%\n", + "22 - 40 | 4.796163069544365%\n", + "19 - 41 | 4.916067146282973%\n", + "18 - 42 | 5.0359712230215825%\n", + "26 - 43 | 5.155875299760192%\n", + "17 - 44 | 5.275779376498801%\n", + "23 - 45 | 5.39568345323741%\n", + "27 - 46 | 5.515587529976019%\n", + "15 - 47 | 5.635491606714628%\n", + "45 - 48 | 5.755395683453238%\n", + "9 - 49 | 5.875299760191846%\n", + "24 - 50 | 5.995203836930456%\n", + "32 - 51 | 6.115107913669065%\n", + "7 - 52 | 6.235011990407674%\n", + "11 - 53 | 6.3549160671462825%\n", + "13 - 54 | 6.474820143884892%\n", + "3 - 55 | 6.594724220623502%\n", + "11 - 56 | 6.71462829736211%\n", + "25 - 57 | 6.83453237410072%\n", + "24 - 58 | 6.954436450839328%\n", + "24 - 59 | 7.074340527577938%\n", + "31 - 60 | 7.194244604316546%\n", + "24 - 61 | 7.314148681055157%\n", + "20 - 62 | 7.434052757793765%\n", + "15 - 63 | 7.553956834532374%\n", + "14 - 64 | 7.673860911270983%\n", + "34 - 65 | 7.793764988009593%\n", + "24 - 66 | 7.913669064748201%\n", + "22 - 67 | 8.03357314148681%\n", + "14 - 68 | 8.15347721822542%\n", + "15 - 69 | 8.273381294964029%\n", + "21 - 70 | 8.393285371702639%\n", + "3 - 71 | 8.513189448441247%\n", + "32 - 72 | 8.633093525179856%\n", + "27 - 73 | 8.752997601918464%\n", + "20 - 74 | 8.872901678657074%\n", + "28 - 75 | 8.992805755395683%\n", + "13 - 76 | 9.112709832134293%\n", + "16 - 77 | 9.232613908872901%\n", + "26 - 78 | 9.352517985611511%\n", + "14 - 79 | 9.47242206235012%\n", + "22 - 80 | 9.59232613908873%\n", + "10 - 81 | 9.712230215827338%\n", + "14 - 82 | 9.832134292565947%\n", + "17 - 83 | 9.952038369304557%\n", + "17 - 84 | 10.071942446043165%\n", + "12 - 85 | 10.191846522781775%\n", + "10 - 86 | 10.311750599520384%\n", + "9 - 87 | 10.431654676258994%\n", + "3 - 88 | 10.551558752997602%\n", + "9 - 89 | 10.67146282973621%\n", + "33 - 90 | 10.79136690647482%\n", + "26 - 91 | 10.911270983213429%\n", + "24 - 92 | 11.031175059952037%\n", + "19 - 93 | 11.151079136690647%\n", + "21 - 94 | 11.270983213429256%\n", + "22 - 95 | 11.390887290167866%\n", + "12 - 96 | 11.510791366906476%\n", + "13 - 97 | 11.630695443645084%\n", + "48 - 98 | 11.750599520383693%\n", + "3 - 99 | 11.870503597122301%\n", + "24 - 100 | 11.990407673860911%\n", + "17 - 101 | 12.11031175059952%\n", + "3 - 102 | 12.23021582733813%\n", + "10 - 103 | 12.350119904076738%\n", + "6 - 104 | 12.470023980815348%\n", + "16 - 105 | 12.589928057553957%\n", + "15 - 106 | 12.709832134292565%\n", + "19 - 107 | 12.829736211031175%\n", + "11 - 108 | 12.949640287769784%\n", + "11 - 109 | 13.069544364508392%\n", + "6 - 110 | 13.189448441247004%\n", + "15 - 111 | 13.309352517985612%\n", + "15 - 112 | 13.42925659472422%\n", + "16 - 113 | 13.549160671462829%\n", + "22 - 114 | 13.66906474820144%\n", + "19 - 115 | 13.788968824940047%\n", + "6 - 116 | 13.908872901678656%\n", + "29 - 117 | 14.028776978417264%\n", + "18 - 118 | 14.148681055155876%\n", + "22 - 119 | 14.268585131894485%\n", + "19 - 120 | 14.388489208633093%\n", + "14 - 121 | 14.508393285371701%\n", + "32 - 122 | 14.628297362110313%\n", + "24 - 123 | 14.748201438848922%\n", + "8 - 124 | 14.86810551558753%\n", + "26 - 125 | 14.98800959232614%\n", + "30 - 126 | 15.107913669064748%\n", + "23 - 127 | 15.227817745803357%\n", + "14 - 128 | 15.347721822541965%\n", + "18 - 129 | 15.467625899280577%\n", + "18 - 130 | 15.587529976019185%\n", + "9 - 131 | 15.707434052757794%\n", + "21 - 132 | 15.827338129496402%\n", + "24 - 133 | 15.947242206235012%\n", + "22 - 134 | 16.06714628297362%\n", + "22 - 135 | 16.18705035971223%\n", + "11 - 136 | 16.30695443645084%\n", + "13 - 137 | 16.426858513189448%\n", + "34 - 138 | 16.546762589928058%\n", + "9 - 139 | 16.666666666666664%\n", + "18 - 140 | 16.786570743405278%\n", + "18 - 141 | 16.906474820143885%\n", + "38 - 142 | 17.026378896882495%\n", + "21 - 143 | 17.1462829736211%\n", + "19 - 144 | 17.26618705035971%\n", + "19 - 145 | 17.38609112709832%\n", + "17 - 146 | 17.505995203836928%\n", + "23 - 147 | 17.62589928057554%\n", + "28 - 148 | 17.74580335731415%\n", + "16 - 149 | 17.86570743405276%\n", + "7 - 150 | 17.985611510791365%\n", + "22 - 151 | 18.10551558752998%\n", + "6 - 152 | 18.225419664268586%\n", + "17 - 153 | 18.345323741007196%\n", + "18 - 154 | 18.465227817745802%\n", + "18 - 155 | 18.585131894484412%\n", + "31 - 156 | 18.705035971223023%\n", + "6 - 157 | 18.82494004796163%\n", + "21 - 158 | 18.94484412470024%\n", + "11 - 159 | 19.06474820143885%\n", + "18 - 160 | 19.18465227817746%\n", + "15 - 161 | 19.304556354916066%\n", + "16 - 162 | 19.424460431654676%\n", + "13 - 163 | 19.544364508393286%\n", + "12 - 164 | 19.664268585131893%\n", + "14 - 165 | 19.784172661870503%\n", + "21 - 166 | 19.904076738609113%\n", + "13 - 167 | 20.023980815347723%\n", + "14 - 168 | 20.14388489208633%\n", + "7 - 169 | 20.26378896882494%\n", + "29 - 170 | 20.38369304556355%\n", + "13 - 171 | 20.503597122302157%\n", + "17 - 172 | 20.623501199040767%\n", + "15 - 173 | 20.743405275779374%\n", + "17 - 174 | 20.863309352517987%\n", + "11 - 175 | 20.983213429256594%\n", + "10 - 176 | 21.103117505995204%\n", + "35 - 177 | 21.223021582733814%\n", + "26 - 178 | 21.34292565947242%\n", + "23 - 179 | 21.46282973621103%\n", + "29 - 180 | 21.58273381294964%\n", + "22 - 181 | 21.70263788968825%\n", + "24 - 182 | 21.822541966426858%\n", + "18 - 183 | 21.942446043165468%\n", + "19 - 184 | 22.062350119904075%\n", + "22 - 185 | 22.18225419664269%\n", + "24 - 186 | 22.302158273381295%\n", + "22 - 187 | 22.422062350119905%\n", + "12 - 188 | 22.54196642685851%\n", + "28 - 189 | 22.66187050359712%\n", + "26 - 190 | 22.781774580335732%\n", + "34 - 191 | 22.90167865707434%\n", + "25 - 192 | 23.021582733812952%\n", + "20 - 193 | 23.14148681055156%\n", + "14 - 194 | 23.26139088729017%\n", + "19 - 195 | 23.381294964028775%\n", + "20 - 196 | 23.501199040767386%\n", + "18 - 197 | 23.621103117505996%\n", + "25 - 198 | 23.741007194244602%\n", + "16 - 199 | 23.860911270983213%\n", + "32 - 200 | 23.980815347721823%\n", + "17 - 201 | 24.100719424460433%\n", + "34 - 202 | 24.22062350119904%\n", + "20 - 203 | 24.34052757793765%\n", + "28 - 204 | 24.46043165467626%\n", + "26 - 205 | 24.580335731414866%\n", + "22 - 206 | 24.700239808153476%\n", + "35 - 207 | 24.820143884892087%\n", + "19 - 208 | 24.940047961630697%\n", + "22 - 209 | 25.059952038369303%\n", + "27 - 210 | 25.179856115107913%\n", + "21 - 211 | 25.29976019184652%\n", + "30 - 212 | 25.41966426858513%\n", + "15 - 213 | 25.539568345323744%\n", + "14 - 214 | 25.65947242206235%\n", + "28 - 215 | 25.77937649880096%\n", + "12 - 216 | 25.899280575539567%\n", + "29 - 217 | 26.019184652278177%\n", + "21 - 218 | 26.139088729016784%\n", + "20 - 219 | 26.258992805755394%\n", + "32 - 220 | 26.378896882494008%\n", + "25 - 221 | 26.498800959232614%\n", + "25 - 222 | 26.618705035971225%\n", + "20 - 223 | 26.73860911270983%\n", + "25 - 224 | 26.85851318944844%\n", + "19 - 225 | 26.978417266187048%\n", + "25 - 226 | 27.098321342925658%\n", + "12 - 227 | 27.218225419664265%\n", + "21 - 228 | 27.33812949640288%\n", + "14 - 229 | 27.45803357314149%\n", + "19 - 230 | 27.577937649880095%\n", + "3 - 231 | 27.697841726618705%\n", + "16 - 232 | 27.81774580335731%\n", + "22 - 233 | 27.937649880095922%\n", + "18 - 234 | 28.05755395683453%\n", + "35 - 235 | 28.177458033573146%\n", + "33 - 236 | 28.297362110311752%\n", + "14 - 237 | 28.41726618705036%\n", + "17 - 238 | 28.53717026378897%\n", + "17 - 239 | 28.65707434052758%\n", + "35 - 240 | 28.776978417266186%\n", + "25 - 241 | 28.896882494004792%\n", + "17 - 242 | 29.016786570743403%\n", + "19 - 243 | 29.136690647482016%\n", + "21 - 244 | 29.256594724220626%\n", + "16 - 245 | 29.376498800959233%\n", + "16 - 246 | 29.496402877697843%\n", + "26 - 247 | 29.61630695443645%\n", + "19 - 248 | 29.73621103117506%\n", + "18 - 249 | 29.856115107913666%\n", + "17 - 250 | 29.97601918465228%\n", + "14 - 251 | 30.09592326139089%\n", + "26 - 252 | 30.215827338129497%\n", + "15 - 253 | 30.335731414868107%\n", + "15 - 254 | 30.455635491606714%\n", + "21 - 255 | 30.575539568345324%\n", + "27 - 256 | 30.69544364508393%\n", + "24 - 257 | 30.815347721822544%\n", + "19 - 258 | 30.935251798561154%\n", + "25 - 259 | 31.05515587529976%\n", + "15 - 260 | 31.17505995203837%\n", + "12 - 261 | 31.294964028776977%\n", + "27 - 262 | 31.414868105515588%\n", + "16 - 263 | 31.534772182254194%\n", + "14 - 264 | 31.654676258992804%\n", + "17 - 265 | 31.774580335731418%\n", + "21 - 266 | 31.894484412470025%\n", + "29 - 267 | 32.014388489208635%\n", + "33 - 268 | 32.13429256594724%\n", + "11 - 269 | 32.25419664268585%\n", + "25 - 270 | 32.37410071942446%\n", + "25 - 271 | 32.49400479616307%\n", + "7 - 272 | 32.61390887290168%\n", + "21 - 273 | 32.73381294964029%\n", + "23 - 274 | 32.853717026378895%\n", + "19 - 275 | 32.97362110311751%\n", + "16 - 276 | 33.093525179856115%\n", + "29 - 277 | 33.21342925659472%\n", + "0 - 278 | 33.33333333333333%\n", + "14 - 279 | 33.45323741007194%\n", + "17 - 280 | 33.573141486810556%\n", + "18 - 281 | 33.69304556354916%\n", + "7 - 282 | 33.81294964028777%\n", + "22 - 283 | 33.932853717026376%\n", + "15 - 284 | 34.05275779376499%\n", + "7 - 285 | 34.172661870503596%\n", + "16 - 286 | 34.2925659472422%\n", + "13 - 287 | 34.412470023980816%\n", + "22 - 288 | 34.53237410071942%\n", + "15 - 289 | 34.65227817745804%\n", + "12 - 290 | 34.77218225419664%\n", + "10 - 291 | 34.89208633093525%\n", + "14 - 292 | 35.011990407673856%\n", + "11 - 293 | 35.13189448441247%\n", + "16 - 294 | 35.25179856115108%\n", + "13 - 295 | 35.37170263788969%\n", + "17 - 296 | 35.4916067146283%\n", + "12 - 297 | 35.61151079136691%\n", + "17 - 298 | 35.73141486810552%\n", + "8 - 299 | 35.851318944844124%\n", + "16 - 300 | 35.97122302158273%\n", + "18 - 301 | 36.09112709832134%\n", + "15 - 302 | 36.21103117505996%\n", + "22 - 303 | 36.330935251798564%\n", + "15 - 304 | 36.45083932853717%\n", + "18 - 305 | 36.57074340527578%\n", + "12 - 306 | 36.69064748201439%\n", + "14 - 307 | 36.810551558753%\n", + "17 - 308 | 36.930455635491604%\n", + "10 - 309 | 37.05035971223021%\n", + "18 - 310 | 37.170263788968825%\n", + "20 - 311 | 37.29016786570744%\n", + "24 - 312 | 37.410071942446045%\n", + "29 - 313 | 37.52997601918465%\n", + "10 - 314 | 37.64988009592326%\n", + "12 - 315 | 37.76978417266187%\n", + "60 - 316 | 37.88968824940048%\n", + "23 - 317 | 38.00959232613909%\n", + "13 - 318 | 38.1294964028777%\n", + "15 - 319 | 38.249400479616305%\n", + "24 - 320 | 38.36930455635492%\n", + "17 - 321 | 38.489208633093526%\n", + "21 - 322 | 38.60911270983213%\n", + "11 - 323 | 38.72901678657074%\n", + "18 - 324 | 38.84892086330935%\n", + "22 - 325 | 38.968824940047966%\n", + "6 - 326 | 39.08872901678657%\n", + "7 - 327 | 39.20863309352518%\n", + "2 - 328 | 39.328537170263786%\n", + "10 - 329 | 39.4484412470024%\n", + "26 - 330 | 39.568345323741006%\n", + "25 - 331 | 39.68824940047961%\n", + "19 - 332 | 39.80815347721823%\n", + "10 - 333 | 39.92805755395683%\n", + "20 - 334 | 40.04796163069545%\n", + "24 - 335 | 40.16786570743405%\n", + "6 - 336 | 40.28776978417266%\n", + "10 - 337 | 40.40767386091127%\n", + "13 - 338 | 40.52757793764988%\n", + "27 - 339 | 40.647482014388494%\n", + "28 - 340 | 40.7673860911271%\n", + "15 - 341 | 40.88729016786571%\n", + "16 - 342 | 41.007194244604314%\n", + "30 - 343 | 41.12709832134293%\n", + "14 - 344 | 41.247002398081534%\n", + "15 - 345 | 41.36690647482014%\n", + "26 - 346 | 41.48681055155875%\n", + "8 - 347 | 41.60671462829736%\n", + "32 - 348 | 41.726618705035975%\n", + "1 - 349 | 41.84652278177458%\n", + "15 - 350 | 41.96642685851319%\n", + "35 - 351 | 42.086330935251794%\n", + "26 - 352 | 42.20623501199041%\n", + "23 - 353 | 42.326139088729015%\n", + "18 - 354 | 42.44604316546763%\n", + "2 - 355 | 42.565947242206235%\n", + "22 - 356 | 42.68585131894484%\n", + "28 - 357 | 42.805755395683455%\n", + "16 - 358 | 42.92565947242206%\n", + "10 - 359 | 43.04556354916067%\n", + "22 - 360 | 43.16546762589928%\n", + "15 - 361 | 43.28537170263789%\n", + "21 - 362 | 43.4052757793765%\n", + "22 - 363 | 43.52517985611511%\n", + "21 - 364 | 43.645083932853716%\n", + "23 - 365 | 43.76498800959233%\n", + "20 - 366 | 43.884892086330936%\n", + "11 - 367 | 44.00479616306954%\n", + "13 - 368 | 44.12470023980815%\n", + "26 - 369 | 44.24460431654676%\n", + "26 - 370 | 44.36450839328538%\n", + "16 - 371 | 44.48441247002398%\n", + "17 - 372 | 44.60431654676259%\n", + "26 - 373 | 44.724220623501196%\n", + "21 - 374 | 44.84412470023981%\n", + "21 - 375 | 44.96402877697842%\n", + "26 - 376 | 45.08393285371702%\n", + "15 - 377 | 45.20383693045564%\n", + "24 - 378 | 45.32374100719424%\n", + "22 - 379 | 45.44364508393286%\n", + "17 - 380 | 45.563549160671464%\n", + "29 - 381 | 45.68345323741007%\n", + "20 - 382 | 45.80335731414868%\n", + "19 - 383 | 45.92326139088729%\n", + "3 - 384 | 46.043165467625904%\n", + "17 - 385 | 46.16306954436451%\n", + "16 - 386 | 46.28297362110312%\n", + "33 - 387 | 46.402877697841724%\n", + "22 - 388 | 46.52278177458034%\n", + "32 - 389 | 46.642685851318944%\n", + "25 - 390 | 46.76258992805755%\n", + "33 - 391 | 46.882494004796165%\n", + "21 - 392 | 47.00239808153477%\n", + "21 - 393 | 47.122302158273385%\n", + "8 - 394 | 47.24220623501199%\n", + "22 - 395 | 47.3621103117506%\n", + "21 - 396 | 47.482014388489205%\n", + "20 - 397 | 47.60191846522782%\n", + "25 - 398 | 47.721822541966425%\n", + "23 - 399 | 47.84172661870504%\n", + "26 - 400 | 47.961630695443645%\n", + "25 - 401 | 48.08153477218225%\n", + "23 - 402 | 48.201438848920866%\n", + "25 - 403 | 48.32134292565947%\n", + "22 - 404 | 48.44124700239808%\n", + "15 - 405 | 48.561151079136685%\n", + "23 - 406 | 48.6810551558753%\n", + "28 - 407 | 48.80095923261391%\n", + "18 - 408 | 48.92086330935252%\n", + "15 - 409 | 49.040767386091126%\n", + "24 - 410 | 49.16067146282973%\n", + "31 - 411 | 49.280575539568346%\n", + "29 - 412 | 49.40047961630695%\n", + "24 - 413 | 49.52038369304556%\n", + "24 - 414 | 49.64028776978417%\n", + "24 - 415 | 49.76019184652279%\n", + "21 - 416 | 49.88009592326139%\n", + "23 - 417 | 50.0%\n", + "32 - 418 | 50.11990407673861%\n", + "21 - 419 | 50.23980815347721%\n", + "30 - 420 | 50.35971223021583%\n", + "26 - 421 | 50.47961630695443%\n", + "29 - 422 | 50.59952038369304%\n", + "19 - 423 | 50.719424460431654%\n", + "28 - 424 | 50.83932853717026%\n", + "37 - 425 | 50.95923261390888%\n", + "24 - 426 | 51.07913669064749%\n", + "23 - 427 | 51.199040767386094%\n", + "22 - 428 | 51.3189448441247%\n", + "20 - 429 | 51.438848920863315%\n", + "36 - 430 | 51.55875299760192%\n", + "18 - 431 | 51.67865707434053%\n", + "16 - 432 | 51.798561151079134%\n", + "7 - 433 | 51.91846522781775%\n", + "29 - 434 | 52.038369304556355%\n", + "27 - 435 | 52.15827338129496%\n", + "19 - 436 | 52.27817745803357%\n", + "2 - 437 | 52.39808153477218%\n", + "18 - 438 | 52.51798561151079%\n", + "10 - 439 | 52.637889688249395%\n", + "19 - 440 | 52.757793764988016%\n", + "27 - 441 | 52.87769784172662%\n", + "16 - 442 | 52.99760191846523%\n", + "11 - 443 | 53.11750599520384%\n", + "20 - 444 | 53.23741007194245%\n", + "25 - 445 | 53.357314148681056%\n", + "5 - 446 | 53.47721822541966%\n", + "9 - 447 | 53.597122302158276%\n", + "6 - 448 | 53.71702637889688%\n", + "17 - 449 | 53.83693045563549%\n", + "4 - 450 | 53.956834532374096%\n", + "26 - 451 | 54.07673860911271%\n", + "7 - 452 | 54.196642685851316%\n", + "6 - 453 | 54.31654676258992%\n", + "11 - 454 | 54.43645083932853%\n", + "8 - 455 | 54.55635491606715%\n", + "25 - 456 | 54.67625899280576%\n", + "3 - 457 | 54.79616306954437%\n", + "13 - 458 | 54.91606714628298%\n", + "11 - 459 | 55.03597122302158%\n", + "20 - 460 | 55.15587529976019%\n", + "13 - 461 | 55.275779376498804%\n", + "9 - 462 | 55.39568345323741%\n", + "7 - 463 | 55.51558752997602%\n", + "9 - 464 | 55.63549160671462%\n", + "24 - 465 | 55.75539568345324%\n", + "9 - 466 | 55.875299760191844%\n", + "15 - 467 | 55.99520383693045%\n", + "14 - 468 | 56.11510791366906%\n", + "17 - 469 | 56.23501199040767%\n", + "33 - 470 | 56.35491606714629%\n", + "12 - 471 | 56.4748201438849%\n", + "36 - 472 | 56.594724220623505%\n", + "21 - 473 | 56.71462829736211%\n", + "26 - 474 | 56.83453237410072%\n", + "12 - 475 | 56.95443645083933%\n", + "27 - 476 | 57.07434052757794%\n", + "31 - 477 | 57.194244604316545%\n", + "28 - 478 | 57.31414868105516%\n", + "21 - 479 | 57.434052757793765%\n", + "17 - 480 | 57.55395683453237%\n", + "27 - 481 | 57.67386091127098%\n", + "27 - 482 | 57.793764988009585%\n", + "25 - 483 | 57.9136690647482%\n", + "19 - 484 | 58.033573141486805%\n", + "9 - 485 | 58.153477218225426%\n", + "27 - 486 | 58.27338129496403%\n", + "22 - 487 | 58.39328537170264%\n", + "21 - 488 | 58.51318944844125%\n", + "15 - 489 | 58.63309352517986%\n", + "24 - 490 | 58.752997601918466%\n", + "29 - 491 | 58.87290167865707%\n", + "22 - 492 | 58.992805755395686%\n", + "23 - 493 | 59.11270983213429%\n", + "26 - 494 | 59.2326139088729%\n", + "32 - 495 | 59.352517985611506%\n", + "16 - 496 | 59.47242206235012%\n", + "24 - 497 | 59.592326139088726%\n", + "17 - 498 | 59.71223021582733%\n", + "17 - 499 | 59.832134292565954%\n", + "31 - 500 | 59.95203836930456%\n", + "22 - 501 | 60.07194244604317%\n", + "30 - 502 | 60.19184652278178%\n", + "20 - 503 | 60.31175059952039%\n", + "16 - 504 | 60.431654676258994%\n", + "19 - 505 | 60.5515587529976%\n", + "26 - 506 | 60.671462829736214%\n", + "30 - 507 | 60.79136690647482%\n", + "19 - 508 | 60.91127098321343%\n", + "22 - 509 | 61.031175059952034%\n", + "19 - 510 | 61.15107913669065%\n", + "11 - 511 | 61.270983213429254%\n", + "18 - 512 | 61.39088729016786%\n", + "18 - 513 | 61.51079136690647%\n", + "17 - 514 | 61.63069544364509%\n", + "15 - 515 | 61.750599520383695%\n", + "26 - 516 | 61.87050359712231%\n", + "18 - 517 | 61.990407673860915%\n", + "15 - 518 | 62.11031175059952%\n", + "18 - 519 | 62.23021582733813%\n", + "32 - 520 | 62.35011990407674%\n", + "26 - 521 | 62.47002398081535%\n", + "20 - 522 | 62.589928057553955%\n", + "26 - 523 | 62.70983213429256%\n", + "5 - 524 | 62.829736211031175%\n", + "22 - 525 | 62.94964028776978%\n", + "31 - 526 | 63.06954436450839%\n", + "27 - 527 | 63.189448441246995%\n", + "30 - 528 | 63.30935251798561%\n", + "22 - 529 | 63.42925659472422%\n", + "23 - 530 | 63.549160671462836%\n", + "15 - 531 | 63.66906474820144%\n", + "9 - 532 | 63.78896882494005%\n", + "14 - 533 | 63.90887290167866%\n", + "22 - 534 | 64.02877697841727%\n", + "21 - 535 | 64.14868105515588%\n", + "19 - 536 | 64.26858513189448%\n", + "26 - 537 | 64.38848920863309%\n", + "19 - 538 | 64.5083932853717%\n", + "21 - 539 | 64.62829736211032%\n", + "24 - 540 | 64.74820143884892%\n", + "23 - 541 | 64.86810551558753%\n", + "24 - 542 | 64.98800959232614%\n", + "19 - 543 | 65.10791366906474%\n", + "22 - 544 | 65.22781774580336%\n", + "32 - 545 | 65.34772182254197%\n", + "19 - 546 | 65.46762589928058%\n", + "25 - 547 | 65.58752997601918%\n", + "22 - 548 | 65.70743405275779%\n", + "23 - 549 | 65.82733812949641%\n", + "11 - 550 | 65.94724220623502%\n", + "22 - 551 | 66.06714628297362%\n", + "21 - 552 | 66.18705035971223%\n", + "23 - 553 | 66.30695443645084%\n", + "26 - 554 | 66.42685851318944%\n", + "10 - 555 | 66.54676258992805%\n", + "0 - 556 | 66.66666666666666%\n", + "22 - 557 | 66.78657074340528%\n", + "20 - 558 | 66.90647482014388%\n", + "9 - 559 | 67.0263788968825%\n", + "22 - 560 | 67.14628297362111%\n", + "8 - 561 | 67.26618705035972%\n", + "1 - 562 | 67.38609112709833%\n", + "1 - 563 | 67.50599520383693%\n", + "20 - 564 | 67.62589928057554%\n", + "13 - 565 | 67.74580335731414%\n", + "8 - 566 | 67.86570743405275%\n", + "5 - 567 | 67.98561151079137%\n", + "27 - 568 | 68.10551558752998%\n", + "4 - 569 | 68.22541966426859%\n", + "15 - 570 | 68.34532374100719%\n", + "4 - 571 | 68.4652278177458%\n", + "7 - 572 | 68.5851318944844%\n", + "20 - 573 | 68.70503597122301%\n", + "12 - 574 | 68.82494004796163%\n", + "9 - 575 | 68.94484412470024%\n", + "15 - 576 | 69.06474820143885%\n", + "13 - 577 | 69.18465227817747%\n", + "27 - 578 | 69.30455635491607%\n", + "27 - 579 | 69.42446043165468%\n", + "5 - 580 | 69.54436450839329%\n", + "10 - 581 | 69.6642685851319%\n", + "18 - 582 | 69.7841726618705%\n", + "4 - 583 | 69.9040767386091%\n", + "22 - 584 | 70.02398081534771%\n", + "12 - 585 | 70.14388489208633%\n", + "21 - 586 | 70.26378896882494%\n", + "10 - 587 | 70.38369304556355%\n", + "14 - 588 | 70.50359712230215%\n", + "31 - 589 | 70.62350119904077%\n", + "18 - 590 | 70.74340527577938%\n", + "14 - 591 | 70.86330935251799%\n", + "17 - 592 | 70.9832134292566%\n", + "21 - 593 | 71.1031175059952%\n", + "19 - 594 | 71.22302158273382%\n", + "18 - 595 | 71.34292565947243%\n", + "23 - 596 | 71.46282973621103%\n", + "20 - 597 | 71.58273381294964%\n", + "18 - 598 | 71.70263788968825%\n", + "15 - 599 | 71.82254196642685%\n", + "14 - 600 | 71.94244604316546%\n", + "18 - 601 | 72.06235011990407%\n", + "5 - 602 | 72.18225419664267%\n", + "21 - 603 | 72.3021582733813%\n", + "20 - 604 | 72.42206235011992%\n", + "23 - 605 | 72.54196642685852%\n", + "13 - 606 | 72.66187050359713%\n", + "30 - 607 | 72.78177458033574%\n", + "27 - 608 | 72.90167865707434%\n", + "11 - 609 | 73.02158273381295%\n", + "16 - 610 | 73.14148681055156%\n", + "21 - 611 | 73.26139088729016%\n", + "18 - 612 | 73.38129496402878%\n", + "23 - 613 | 73.50119904076739%\n", + "13 - 614 | 73.621103117506%\n", + "18 - 615 | 73.7410071942446%\n", + "27 - 616 | 73.86091127098321%\n", + "12 - 617 | 73.98081534772182%\n", + "25 - 618 | 74.10071942446042%\n", + "22 - 619 | 74.22062350119904%\n", + "21 - 620 | 74.34052757793765%\n", + "16 - 621 | 74.46043165467626%\n", + "18 - 622 | 74.58033573141488%\n", + "18 - 623 | 74.70023980815348%\n", + "17 - 624 | 74.82014388489209%\n", + "21 - 625 | 74.9400479616307%\n", + "9 - 626 | 75.0599520383693%\n", + "22 - 627 | 75.17985611510791%\n", + "18 - 628 | 75.29976019184652%\n", + "13 - 629 | 75.41966426858512%\n", + "15 - 630 | 75.53956834532374%\n", + "24 - 631 | 75.65947242206235%\n", + "22 - 632 | 75.77937649880096%\n", + "28 - 633 | 75.89928057553958%\n", + "6 - 634 | 76.01918465227818%\n", + "21 - 635 | 76.13908872901679%\n", + "19 - 636 | 76.2589928057554%\n", + "8 - 637 | 76.378896882494%\n", + "22 - 638 | 76.49880095923261%\n", + "25 - 639 | 76.61870503597122%\n", + "19 - 640 | 76.73860911270984%\n", + "23 - 641 | 76.85851318944844%\n", + "28 - 642 | 76.97841726618705%\n", + "23 - 643 | 77.09832134292566%\n", + "26 - 644 | 77.21822541966426%\n", + "16 - 645 | 77.33812949640287%\n", + "12 - 646 | 77.45803357314148%\n", + "18 - 647 | 77.57793764988008%\n", + "22 - 648 | 77.6978417266187%\n", + "19 - 649 | 77.81774580335733%\n", + "10 - 650 | 77.93764988009593%\n", + "19 - 651 | 78.05755395683454%\n", + "16 - 652 | 78.17745803357315%\n", + "18 - 653 | 78.29736211031175%\n", + "14 - 654 | 78.41726618705036%\n", + "16 - 655 | 78.53717026378897%\n", + "23 - 656 | 78.65707434052757%\n", + "20 - 657 | 78.77697841726618%\n", + "21 - 658 | 78.8968824940048%\n", + "31 - 659 | 79.0167865707434%\n", + "5 - 660 | 79.13669064748201%\n", + "25 - 661 | 79.25659472422062%\n", + "24 - 662 | 79.37649880095923%\n", + "8 - 663 | 79.49640287769785%\n", + "27 - 664 | 79.61630695443645%\n", + "29 - 665 | 79.73621103117506%\n", + "20 - 666 | 79.85611510791367%\n", + "16 - 667 | 79.97601918465229%\n", + "16 - 668 | 80.0959232613909%\n", + "24 - 669 | 80.2158273381295%\n", + "19 - 670 | 80.3357314148681%\n", + "20 - 671 | 80.45563549160671%\n", + "26 - 672 | 80.57553956834532%\n", + "24 - 673 | 80.69544364508393%\n", + "24 - 674 | 80.81534772182253%\n", + "21 - 675 | 80.93525179856115%\n", + "9 - 676 | 81.05515587529976%\n", + "12 - 677 | 81.17505995203837%\n", + "28 - 678 | 81.29496402877699%\n", + "11 - 679 | 81.4148681055156%\n", + "25 - 680 | 81.5347721822542%\n", + "14 - 681 | 81.65467625899281%\n", + "30 - 682 | 81.77458033573141%\n", + "6 - 683 | 81.89448441247002%\n", + "27 - 684 | 82.01438848920863%\n", + "20 - 685 | 82.13429256594725%\n", + "22 - 686 | 82.25419664268586%\n", + "18 - 687 | 82.37410071942446%\n", + "1 - 688 | 82.49400479616307%\n", + "25 - 689 | 82.61390887290167%\n", + "17 - 690 | 82.73381294964028%\n", + "23 - 691 | 82.85371702637889%\n", + "22 - 692 | 82.9736211031175%\n", + "21 - 693 | 83.09352517985612%\n", + "27 - 694 | 83.21342925659472%\n", + "20 - 695 | 83.33333333333334%\n", + "26 - 696 | 83.45323741007195%\n", + "20 - 697 | 83.57314148681056%\n", + "25 - 698 | 83.69304556354916%\n", + "6 - 699 | 83.81294964028777%\n", + "26 - 700 | 83.93285371702638%\n", + "16 - 701 | 84.05275779376498%\n", + "15 - 702 | 84.17266187050359%\n", + "36 - 703 | 84.29256594724221%\n", + "19 - 704 | 84.41247002398082%\n", + "20 - 705 | 84.53237410071942%\n", + "18 - 706 | 84.65227817745803%\n", + "22 - 707 | 84.77218225419664%\n", + "24 - 708 | 84.89208633093526%\n", + "26 - 709 | 85.01199040767386%\n", + "16 - 710 | 85.13189448441247%\n", + "24 - 711 | 85.25179856115108%\n", + "31 - 712 | 85.37170263788968%\n", + "17 - 713 | 85.4916067146283%\n", + "23 - 714 | 85.61151079136691%\n", + "27 - 715 | 85.73141486810552%\n", + "20 - 716 | 85.85131894484412%\n", + "24 - 717 | 85.97122302158273%\n", + "8 - 718 | 86.09112709832134%\n", + "16 - 719 | 86.21103117505994%\n", + "19 - 720 | 86.33093525179856%\n", + "14 - 721 | 86.45083932853717%\n", + "5 - 722 | 86.57074340527578%\n", + "2 - 723 | 86.6906474820144%\n", + "12 - 724 | 86.810551558753%\n", + "4 - 725 | 86.93045563549161%\n", + "8 - 726 | 87.05035971223022%\n", + "4 - 727 | 87.17026378896882%\n", + "11 - 728 | 87.29016786570743%\n", + "6 - 729 | 87.41007194244604%\n", + "4 - 730 | 87.52997601918466%\n", + "13 - 731 | 87.64988009592327%\n", + "0 - 732 | 87.76978417266187%\n", + "6 - 733 | 87.88968824940048%\n", + "1 - 734 | 88.00959232613909%\n", + "13 - 735 | 88.12949640287769%\n", + "14 - 736 | 88.2494004796163%\n", + "7 - 737 | 88.3693045563549%\n", + "21 - 738 | 88.48920863309353%\n", + "20 - 739 | 88.60911270983213%\n", + "31 - 740 | 88.72901678657075%\n", + "21 - 741 | 88.84892086330936%\n", + "26 - 742 | 88.96882494004797%\n", + "18 - 743 | 89.08872901678657%\n", + "22 - 744 | 89.20863309352518%\n", + "13 - 745 | 89.32853717026379%\n", + "24 - 746 | 89.44844124700239%\n", + "30 - 747 | 89.568345323741%\n", + "24 - 748 | 89.68824940047962%\n", + "19 - 749 | 89.80815347721823%\n", + "13 - 750 | 89.92805755395683%\n", + "11 - 751 | 90.04796163069544%\n", + "14 - 752 | 90.16786570743405%\n", + "22 - 753 | 90.28776978417267%\n", + "28 - 754 | 90.40767386091127%\n", + "31 - 755 | 90.52757793764988%\n", + "31 - 756 | 90.64748201438849%\n", + "20 - 757 | 90.7673860911271%\n", + "27 - 758 | 90.88729016786571%\n", + "28 - 759 | 91.00719424460432%\n", + "19 - 760 | 91.12709832134293%\n", + "16 - 761 | 91.24700239808153%\n", + "26 - 762 | 91.36690647482014%\n", + "31 - 763 | 91.48681055155875%\n", + "24 - 764 | 91.60671462829735%\n", + "34 - 765 | 91.72661870503596%\n", + "18 - 766 | 91.84652278177458%\n", + "30 - 767 | 91.96642685851319%\n", + "22 - 768 | 92.08633093525181%\n", + "23 - 769 | 92.20623501199042%\n", + "23 - 770 | 92.32613908872902%\n", + "32 - 771 | 92.44604316546763%\n", + "33 - 772 | 92.56594724220624%\n", + "20 - 773 | 92.68585131894484%\n", + "13 - 774 | 92.80575539568345%\n", + "24 - 775 | 92.92565947242207%\n", + "20 - 776 | 93.04556354916068%\n", + "32 - 777 | 93.16546762589928%\n", + "22 - 778 | 93.28537170263789%\n", + "24 - 779 | 93.4052757793765%\n", + "25 - 780 | 93.5251798561151%\n", + "21 - 781 | 93.64508393285371%\n", + "24 - 782 | 93.76498800959233%\n", + "21 - 783 | 93.88489208633094%\n", + "23 - 784 | 94.00479616306954%\n", + "37 - 785 | 94.12470023980816%\n", + "16 - 786 | 94.24460431654677%\n", + "27 - 787 | 94.36450839328538%\n", + "18 - 788 | 94.48441247002398%\n", + "19 - 789 | 94.60431654676259%\n", + "16 - 790 | 94.7242206235012%\n", + "9 - 791 | 94.8441247002398%\n", + "20 - 792 | 94.96402877697841%\n", + "20 - 793 | 95.08393285371703%\n", + "21 - 794 | 95.20383693045564%\n", + "18 - 795 | 95.32374100719424%\n", + "18 - 796 | 95.44364508393285%\n", + "17 - 797 | 95.56354916067147%\n", + "24 - 798 | 95.68345323741008%\n", + "29 - 799 | 95.80335731414868%\n", + "16 - 800 | 95.92326139088729%\n", + "31 - 801 | 96.0431654676259%\n", + "24 - 802 | 96.1630695443645%\n", + "30 - 803 | 96.28297362110312%\n", + "19 - 804 | 96.40287769784173%\n", + "17 - 805 | 96.52278177458034%\n", + "11 - 806 | 96.64268585131894%\n", + "28 - 807 | 96.76258992805755%\n", + "33 - 808 | 96.88249400479616%\n", + "24 - 809 | 97.00239808153476%\n", + "17 - 810 | 97.12230215827337%\n", + "24 - 811 | 97.24220623501199%\n", + "27 - 812 | 97.3621103117506%\n", + "24 - 813 | 97.48201438848922%\n", + "24 - 814 | 97.60191846522783%\n", + "15 - 815 | 97.72182254196643%\n", + "16 - 816 | 97.84172661870504%\n", + "9 - 817 | 97.96163069544365%\n", + "12 - 818 | 98.08153477218225%\n", + "11 - 819 | 98.20143884892086%\n", + "0 - 820 | 98.32134292565947%\n", + "27 - 821 | 98.44124700239809%\n", + "12 - 822 | 98.56115107913669%\n", + "17 - 823 | 98.6810551558753%\n", + "11 - 824 | 98.8009592326139%\n", + "10 - 825 | 98.92086330935251%\n", + "11 - 826 | 99.04076738609112%\n", + "6 - 827 | 99.16067146282974%\n", + "33 - 828 | 99.28057553956835%\n", + "19 - 829 | 99.40047961630695%\n", + "6 - 830 | 99.52038369304557%\n", + "14 - 831 | 99.64028776978418%\n", + "7 - 832 | 99.76019184652279%\n", + "11 - 833 | 99.8800959232614%\n" + ] + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + }, + "id": "dR_UuC-lvEn5", + "outputId": "70a86ba4-3a26-4158-e53a-7909ece95b38" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
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
\n", + "

5 rows × 53 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "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? ... \\\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]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305 + }, + "id": "M0cQ547ivEn6", + "outputId": "18868b57-d705-4f33-bf04-17937daf9a8b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\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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\n", + "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/" + }, + "id": "OSdmUudLvEn7", + "outputId": "486d98c4-e789-4450-a818-9afe649da05e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n", + " 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n", + " 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n", + " 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1,\n", + " 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n", + " 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n", + " 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n", + " 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n", + " 1, 1, 0, 1, 1, 0, 0, 1, 1])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jDhWSmiyvEn7", + "outputId": "dc797e96-7e8e-4f49-a0f7-27b89bd72fc7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82234616, 0.17765384],\n", + " [0.06307788, 0.93692212],\n", + " [0.96424636, 0.03575364],\n", + " [0.18783281, 0.81216719],\n", + " [0.19107317, 0.80892683],\n", + " [0.9434401 , 0.0565599 ],\n", + " [0.55484673, 0.44515327],\n", + " [0.81912638, 0.18087362],\n", + " [0.1072045 , 0.8927955 ],\n", + " [0.41184308, 0.58815692],\n", + " [0.02048731, 0.97951269],\n", + " [0.7882086 , 0.2117914 ],\n", + " [0.12182653, 0.87817347],\n", + " [0.16023543, 0.83976457],\n", + " [0.21667676, 0.78332324],\n", + " [0.13156947, 0.86843053],\n", + " [0.06477248, 0.93522752],\n", + " [0.55086252, 0.44913748],\n", + " [0.0583293 , 0.9416707 ],\n", + " [0.20576939, 0.79423061],\n", + " [0.03236921, 0.96763079],\n", + " [0.0346709 , 0.9653291 ],\n", + " [0.33333473, 0.66666527],\n", + " [0.44441443, 0.55558557],\n", + " [0.50592871, 0.49407129],\n", + " [0.75879899, 0.24120101],\n", + " [0.09265759, 0.90734241],\n", + " [0.84428435, 0.15571565],\n", + " [0.3508184 , 0.6491816 ],\n", + " [0.03358033, 0.96641967],\n", + " [0.67638544, 0.32361456],\n", + " [0.15415506, 0.84584494],\n", + " [0.0483945 , 0.9516055 ],\n", + " [0.30632733, 0.69367267],\n", + " [0.01226129, 0.98773871],\n", + " [0.03762136, 0.96237864],\n", + " [0.17297269, 0.82702731],\n", + " [0.04825099, 0.95174901],\n", + " [0.29380817, 0.70619183],\n", + " [0.1828379 , 0.8171621 ],\n", + " [0.92024525, 0.07975475],\n", + " [0.5601304 , 0.4398696 ],\n", + " [0.38669169, 0.61330831],\n", + " [0.08303444, 0.91696556],\n", + " [0.79898013, 0.20101987],\n", + " [0.82445886, 0.17554114],\n", + " [0.88487636, 0.11512364],\n", + " [0.19895148, 0.80104852],\n", + " [0.60266966, 0.39733034],\n", + " [0.07435514, 0.92564486],\n", + " [0.32166569, 0.67833431],\n", + " [0.06991262, 0.93008738],\n", + " [0.34722638, 0.65277362],\n", + " [0.06427689, 0.93572311],\n", + " [0.13798003, 0.86201997],\n", + " [0.03360987, 0.96639013],\n", + " [0.19616494, 0.80383506],\n", + " [0.01002472, 0.98997528],\n", + " [0.05835283, 0.94164717],\n", + " [0.21191527, 0.78808473],\n", + " [0.1507826 , 0.8492174 ],\n", + " [0.88330863, 0.11669137],\n", + " [0.0291325 , 0.9708675 ],\n", + " [0.7170157 , 0.2829843 ],\n", + " [0.01534312, 0.98465688],\n", + " [0.56213071, 0.43786929],\n", + " [0.94671454, 0.05328546],\n", + " [0.06584012, 0.93415988],\n", + " [0.13240139, 0.86759861],\n", + " [0.09623347, 0.90376653],\n", + " [0.45788377, 0.54211623],\n", + " [0.02391387, 0.97608613],\n", + " [0.07435514, 0.92564486],\n", + " [0.2930104 , 0.7069896 ],\n", + " [0.14985398, 0.85014602],\n", + " [0.03657829, 0.96342171],\n", + " [0.42697613, 0.57302387],\n", + " [0.39160255, 0.60839745],\n", + " [0.1670076 , 0.8329924 ],\n", + " [0.07544565, 0.92455435],\n", + " [0.09664869, 0.90335131],\n", + " [0.59148405, 0.40851595],\n", + " [0.42363057, 0.57636943],\n", + " [0.05814156, 0.94185844],\n", + " [0.08359175, 0.91640825],\n", + " [0.13920724, 0.86079276],\n", + " [0.09600658, 0.90399342],\n", + " [0.06056381, 0.93943619],\n", + " [0.47390799, 0.52609201],\n", + " [0.07999946, 0.92000054],\n", + " [0.24779528, 0.75220472],\n", + " [0.70595916, 0.29404084],\n", + " [0.05371939, 0.94628061],\n", + " [0.01487627, 0.98512373],\n", + " [0.04253436, 0.95746564],\n", + " [0.13924034, 0.86075966],\n", + " [0.42128822, 0.57871178],\n", + " [0.04282959, 0.95717041],\n", + " [0.47224425, 0.52775575],\n", + " [0.16948883, 0.83051117],\n", + " [0.03197784, 0.96802216],\n", + " [0.35901526, 0.64098474],\n", + " [0.20758217, 0.79241783],\n", + " [0.91030031, 0.08969969],\n", + " [0.00597444, 0.99402556],\n", + " [0.69070435, 0.30929565],\n", + " [0.89116906, 0.10883094],\n", + " [0.83133686, 0.16866314],\n", + " [0.11045721, 0.88954279],\n", + " [0.31088344, 0.68911656],\n", + " [0.25764591, 0.74235409],\n", + " [0.04159555, 0.95840445],\n", + " [0.93750904, 0.06249096],\n", + " [0.07227873, 0.92772127],\n", + " [0.1765021 , 0.8234979 ],\n", + " [0.22033295, 0.77966705],\n", + " [0.14716117, 0.85283883],\n", + " [0.14660805, 0.85339195],\n", + " [0.58137886, 0.41862114],\n", + " [0.76092709, 0.23907291],\n", + " [0.55551717, 0.44448283],\n", + " [0.11376826, 0.88623174],\n", + " [0.01884213, 0.98115787],\n", + " [0.17813862, 0.82186138],\n", + " [0.09926783, 0.90073217],\n", + " [0.52263501, 0.47736499],\n", + " [0.46877349, 0.53122651],\n", + " [0.15835725, 0.84164275],\n", + " [0.03390345, 0.96609655],\n", + " [0.87835355, 0.12164645],\n", + " [0.16867701, 0.83132299],\n", + " [0.30658276, 0.69341724],\n", + " [0.23006774, 0.76993226],\n", + " [0.24310637, 0.75689363],\n", + " [0.84791496, 0.15208504],\n", + " [0.75750355, 0.24249645],\n", + " [0.27008356, 0.72991644],\n", + " [0.13013537, 0.86986463],\n", + " [0.23091842, 0.76908158],\n", + " [0.27048221, 0.72951779],\n", + " [0.16894754, 0.83105246],\n", + " [0.96995833, 0.03004167],\n", + " [0.12917989, 0.87082011],\n", + " [0.01824254, 0.98175746],\n", + " [0.87271569, 0.12728431],\n", + " [0.53170986, 0.46829014],\n", + " [0.07354943, 0.92645057],\n", + " [0.28438621, 0.71561379],\n", + " [0.47706049, 0.52293951],\n", + " [0.12752634, 0.87247366],\n", + " [0.11194588, 0.88805412],\n", + " [0.62439718, 0.37560282],\n", + " [0.91721176, 0.08278824],\n", + " [0.10225322, 0.89774678],\n", + " [0.04494549, 0.95505451],\n", + " [0.32262195, 0.67737805],\n", + " [0.59701123, 0.40298877],\n", + " [0.10423391, 0.89576609],\n", + " [0.0985215 , 0.9014785 ],\n", + " [0.32108129, 0.67891871],\n", + " [0.17787615, 0.82212385],\n", + " [0.36747563, 0.63252437],\n", + " [0.02665584, 0.97334416],\n", + " [0.84638624, 0.15361376],\n", + " [0.13568134, 0.86431866],\n", + " [0.34720427, 0.65279573],\n", + " [0.25013453, 0.74986547],\n", + " [0.51789923, 0.48210077],\n", + " [0.7353734 , 0.2646266 ],\n", + " [0.17852346, 0.82147654],\n", + " [0.05470265, 0.94529735],\n", + " [0.61108832, 0.38891168],\n", + " [0.01044043, 0.98955957],\n", + " [0.08132003, 0.91867997],\n", + " [0.07805188, 0.92194812],\n", + " [0.05547742, 0.94452258],\n", + " [0.44252454, 0.55747546],\n", + " [0.14823741, 0.85176259],\n", + " [0.18724102, 0.81275898],\n", + " [0.14486682, 0.85513318],\n", + " [0.41931605, 0.58068395],\n", + " [0.87617426, 0.12382574],\n", + " [0.87622721, 0.12377279],\n", + " [0.22673265, 0.77326735],\n", + " [0.06130977, 0.93869023],\n", + " [0.03554123, 0.96445877],\n", + " [0.02134011, 0.97865989],\n", + " [0.37965862, 0.62034138],\n", + " [0.36962224, 0.63037776],\n", + " [0.91640492, 0.08359508],\n", + " [0.23927717, 0.76072283],\n", + " [0.05371993, 0.94628007],\n", + " [0.6245569 , 0.3754431 ],\n", + " [0.38758282, 0.61241718],\n", + " [0.07253408, 0.92746592],\n", + " [0.90478051, 0.09521949],\n", + " [0.26752097, 0.73247903],\n", + " [0.70319731, 0.29680269],\n", + " [0.57033991, 0.42966009],\n", + " [0.75732706, 0.24267294],\n", + " [0.21586125, 0.78413875],\n", + " [0.63084298, 0.36915702],\n", + " [0.40181253, 0.59818747],\n", + " [0.69888551, 0.30111449],\n", + " [0.40503751, 0.59496249],\n", + " [0.71339625, 0.28660375],\n", + " [0.1383278 , 0.8616722 ],\n", + " [0.06324957, 0.93675043],\n", + " [0.23447239, 0.76552761],\n", + " [0.40893054, 0.59106946],\n", + " [0.17825 , 0.82175 ],\n", + " [0.32258485, 0.67741515],\n", + " [0.16166068, 0.83833932],\n", + " [0.69528235, 0.30471765],\n", + " [0.17789165, 0.82210835],\n", + " [0.66764257, 0.33235743],\n", + " [0.27375571, 0.72624429],\n", + " [0.22346341, 0.77653659],\n", + " [0.39947093, 0.60052907],\n", + " [0.46635095, 0.53364905],\n", + " [0.1924168 , 0.8075832 ],\n", + " [0.16711888, 0.83288112],\n", + " [0.21106693, 0.78893307],\n", + " [0.43333522, 0.56666478],\n", + " [0.14573084, 0.85426916],\n", + " [0.02024659, 0.97975341],\n", + " [0.02044819, 0.97955181],\n", + " [0.33611013, 0.66388987],\n", + " [0.98510196, 0.01489804],\n", + " [0.88311867, 0.11688133],\n", + " [0.2810199 , 0.7189801 ],\n", + " [0.07333621, 0.92666379],\n", + " [0.66206539, 0.33793461],\n", + " [0.20077592, 0.79922408],\n", + " [0.17945211, 0.82054789],\n", + " [0.01938447, 0.98061553],\n", + " [0.03658439, 0.96341561],\n", + " [0.49053046, 0.50946954],\n", + " [0.04946739, 0.95053261],\n", + " [0.04069617, 0.95930383],\n", + " [0.66050803, 0.33949197],\n", + " [0.05499123, 0.94500877],\n", + " [0.46033028, 0.53966972],\n", + " [0.39059674, 0.60940326],\n", + " [0.95003158, 0.04996842],\n", + " [0.2349215 , 0.7650785 ],\n", + " [0.09244606, 0.90755394],\n", + " [0.764769 , 0.235231 ],\n", + " [0.83615536, 0.16384464],\n", + " [0.23655627, 0.76344373],\n", + " [0.05573534, 0.94426466]])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Twh075x1vEn7", + "outputId": "aacce33b-4a4e-4bfd-ec15-b80439a14821" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "73 0\n", + "551 1\n", + "117 0\n", + "372 1\n", + "216 1\n", + " ..\n", + "344 1\n", + "40 1\n", + "89 0\n", + "453 1\n", + "605 1\n", + "Name: É Bot?, Length: 251, dtype: int64" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HvtvdS0rvEn7", + "outputId": "c9935c3f-5b61-4e83-acac-76920013d36a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7796670548979613" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LqsOJwBLvEn8", + "outputId": "39f38cf0-3293-420a-cd65-235b06f9bca7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7609561752988048" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "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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\n", + "text/plain": [ + "
" + ] + }, + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Não
count84.000000
mean0.479382
std0.284891
min0.014898
25%0.232253
50%0.475195
75%0.750450
max0.973344
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " Não\n", + "count 84.000000\n", + "mean 0.479382\n", + "std 0.284891\n", + "min 0.014898\n", + "25% 0.232253\n", + "50% 0.475195\n", + "75% 0.750450\n", + "max 0.973344" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame({\"Não\": res_nao}).describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "_ayLrQFJvEn8", + "outputId": "3de7e149-8e7b-4bc4-8427-d23439a65c4b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Sim
count167.000000
mean0.767758
std0.233861
min0.030042
25%0.678627
50%0.850146
75%0.938179
max0.994026
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " Sim\n", + "count 167.000000\n", + "mean 0.767758\n", + "std 0.233861\n", + "min 0.030042\n", + "25% 0.678627\n", + "50% 0.850146\n", + "75% 0.938179\n", + "max 0.994026" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 496 + }, + "id": "NADjnw5qvEn8", + "outputId": "978ef88a-11cb-425b-9b51-2e720b1b8162" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1074\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\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": "markdown", + "source": [ + "

Objetivo

" + ], + "metadata": { + "id": "i39ZByArfdgp" + } + }, + { + "cell_type": "markdown", + "source": [ + "Criar um novo atributo que possa ser utilizado no motor aplicando análise de sentimento para indicar com base no texto do twitte se ele é agresssivo ou não.\n", + "\n", + "Para esse primeiro teste fazer merge dos dataframes já gerados (df_handles, df_users e df_result_text) de forma a obter um novo dataframe que contenha os seguintes atributos: 'Comportamento agressivo?', 'tweet_author', 'tweet_text_y'" + ], + "metadata": { + "id": "3OAukWtyfghJ" + } + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Djeb7PUI77DC", + "outputId": "a576754b-1bad-4253-8c70-0b8d770f90d7" + }, + "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: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\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: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\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: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->wordcloud) (2.8.2)\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: 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": 305, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "joxis7kss1II", + "outputId": "08af6cc8-df14-4bd6-e50a-8bdfa9d9e843" + }, + "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" + ] + } + ], + "source": [ + "import re\n", + "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 nltk.stem import WordNetLemmatizer\n", + "from nltk.test.portuguese_en_fixt import setup_module\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\n", + "from scipy.stats import ks_2samp\n", + "from imblearn.over_sampling import SMOTE" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LHgK1OAttT08", + "outputId": "d39de66b-4cba-4039-ced8-3abec0cf8c73" + }, + "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": 306 + } + ], + "source": [ + "df_handles['Comportamento agressivo?'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "UixBJa39kLDg", + "outputId": "6fa2ae84-ce17-4b11-af4f-5247b0bd3478" + }, + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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...
\n", + "

1 rows × 36 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 307 + } + ], + "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", + "source": [ + "e_bot = df_result_merge_text['É Bot?'].unique()\n", + "e_bot" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g4GWWJQFrRTG", + "outputId": "48a417d6-8864-4ec5-9168-99c298758556" + }, + "execution_count": 313, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['não', 'sim', 'inexistente / inacessível', 'sim '], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 313 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_result_merge_text['É Bot?'] = df_result_merge_text['É Bot?'].str.lower()" + ], + "metadata": { + "id": "0eZy8ifvqURb" + }, + "execution_count": 311, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_result_merge_text['É Bot?'].replace('sim', 'sim ', inplace = True)" + ], + "metadata": { + "id": "lZaW9at_r9bs" + }, + "execution_count": 314, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_merge_grafico = sns.countplot(x='É Bot?', data=df_result_merge_text, palette=\"PRGn\")\n", + "df_merge_grafico.set_title('É bot? Sim ou Não')\n", + "df_merge_grafico.set_ylabel('count')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 312 + }, + "id": "yEj5GS1Cpm2J", + "outputId": "aa58ecd6-63d0-42f4-b653-18454dc9b0a3" + }, + "execution_count": 316, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'count')" + ] + }, + "metadata": {}, + "execution_count": 316 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_merge_grafico_agressivo = sns.countplot(x='Comportamento agressivo?', data=df_result_merge_text, palette=\"PRGn\")\n", + "df_merge_grafico_agressivo.set_title('É Agressivo? Sim ou Não')\n", + "df_merge_grafico_agressivo.set_ylabel('count')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "K5agMGAQseS2", + "outputId": "a8d98fc0-65a5-4765-d120-b90e2b4b18a3" + }, + "execution_count": 318, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'count')" + ] + }, + "metadata": {}, + "execution_count": 318 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "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'] = df_result_merge_text['tweet_text'].str.lower()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3DxHVYbVpF6l" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l0iDtJtBns0Q", + "outputId": "13d96090-990c-42f8-c3e7-dd34723b2595" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Dimensões:\n", + "\n", + "Shape: (834, 3)\n", + "\n", + "Quantidade de dados faltantes:\n", + "\n", + "Comportamento agressivo? 0\n", + "tweet_author 0\n", + "tweet_text 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "df_result_merge_text_analise=df_result_merge_text[['Comportamento agressivo?', 'tweet_author', 'tweet_text']]\n", + "print(\"\\nDimensões:\\n\")\n", + "print(\"Shape:\", df_result_merge_text_analise.shape)\n", + "print(\"\\nQuantidade de dados faltantes:\\n\")\n", + "print(df_result_merge_text_analise.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 345 + }, + "id": "es3Nmym3vG7o", + "outputId": "d3e49e0b-3b1e-4593-f0b7-02bf5836d522" + }, + "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", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comportamento agressivo?tweet_authortweet_textTamanho
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
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 321 + } + ], + "source": [ + "df_result_merge_text_analise['Tamanho'] = df_result_merge_text_analise['tweet_text'].apply(len)\n", + "print(\"Tamanho dos comentários:\\n\")\n", + "df_result_merge_text_analise.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "id": "ojUpwQTOvUFn", + "outputId": "3a2ff08b-e374-420b-e09b-0b1faa08d54e" + }, + "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": 322 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnoAAAGDCAYAAAC8371AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUVf7H8fc3jU4goUgJPYA0pQoiLoIFUMECit1dWXTtbW37c0VdVl27C5a4VnRFUcQIiIoI0hQCSO81NAm9lyTn98fcuDGmAZncZObzep55nLnl3O+dgHxy7jn3mnMOEREREQk9EX4XICIiIiLBoaAnIiIiEqIU9ERERERClIKeiIiISIhS0BMREREJUQp6IiIiIiFKQU8kzJnZ62b2aBG1Vc/M9ptZpPd5spkNOsG2HjGz/xRy22/NbJp3/NEncrw82j3h+ksqM2tgZs7MokpT2yJyYhT0REKYma0zs0Nmts/MdpvZDDO7xcx+/bvvnLvFOfdkIds6N79tnHMbnHMVnXMZJ1u7c+6fzrkCQ5aZxQEbgceAz4B3TvbYJ8vMupvZRr/rKE3M7F0z+0dJa0uktNNvXSKh72Ln3EQziwX+ALwMnAH8sSgPYmZRzrn0omyzMJxzO/nfuXQs7uOLiJRk6tETCRPOuT3OuWTgSuAGM2sFv+39MLNqZjbW6/3baWZTzSzCzEYA9YAvvUuzD2S7THeTmW0AJuVx6a6xmc0ys71m9oXXA5drr1f2XkMzG2JmH2Rbd5bXI7nbzFLN7EZv+YVmNs9rP9XMhuRos6+ZLfb2m2xmp+b1HZnZeWa2zMz2mNkwwLKtizCz/zOz9Wa2zcze98JzzjYqAF8Btb3var+Z1TazTmY206tji5kNM7OYbPs5M7vVzFZ6PbBPmllj75z3mtknWdubWVXv55RmZru893WztTXZ23+619Y3ZlYtR6nXmNkGM9tuZn/Ltm8ZM3vJzDZ7r5fMrEwe31ekmT3ntbEGuDDH+tpmluz9WVplZn/Oo53BwDXAA9739WW2/T/zznOtmd3pLY8zs41mdrH3uaLX/vX5tHWq973s9v489M2tFpGQ45zTSy+9QvQFrAPOzWX5BuAv3vt3gX94758CXgeivVc3wHJrC2gAOOB9oAJQLtuyKG+bycAmoJW3zWfAB9667sDGvOoFhmTbtj6wD7jKqyseOD1bO60J/OLaBvgFuMRb1xQ4AJzn7fcAsAqIyeU7qeYdo7+37T1AOjDIW/8nb99GQEVgNDAij+89t3NrD3QmcCWlAbAUuDvbegd8AVQGWgJHgO+848UCS4AbvG3jgcuB8kAlYBQwJltbk4HV3vmX8z4/nePn9qa37jTvWKd6658AfgRqANWBGcCTeZznLcAyIAGIA77P8fP/AXgVKAucDqQBPfJo6128P4fe5whgDvB3IMb7HtYAF3jrzwe2enW+CXyaT1vR3s/uEa+tHt7Pupnff0f10ivYL/XoiYSnzQT+Yc7pGFALqO+cO+acm+qcK+iB2EOccwecc4fyWD/CObfIOXcAeBS4wrzJGsfhamCic+4jr64dzrmfAZxzk51zC51zmc65BcBHBC5RQ6D3cpxz7lvn3DHgOQLh5sxcjtEHWOyc+9Tb9iUCQSLLNcALzrk1zrn9wMPAQCvkxAPn3Bzn3I/OuXTn3DrgjWx1ZvmXc26vc24xsAj4xjveHgK9hG29tnY45z5zzh10zu0DhubS1jvOuRXez+UTAkEru8edc4ecc/OB+QQCX9Z5PuGc2+acSwMeB67L47SuAF5yzqW6wCX0p7JWmFkC0BV40Dl32Pt5/Qe4vhBfFwQuw1d3zj3hnDvqnFtDINAN9L6DbwgE3O8I/OxuzqetzgTC+dNeW5OAsQR+cRAJaQp6IuGpDrAzl+XPEuj5+MbM1pjZQ4VoK/U41q8n0LuS8zJiQRII9FD9jpmdYWbfe5f39hDoZcpqv7Z3TACcc5lePXVyaap29lq9gJuaY/36bJ/XE+idq1mYEzCzpt4l1q1mthf4J7//Hn7J9v5QLp8rem2VN7M3vMvIewn0nFXJEaCzh9SDWfsWYn1u51k7j9P6zXeWY7/awE4viGZfn9t3n5v6BC5/7856EeiRy/59JxHoLX7XObcjn7ZqA6nez/9EahEptRT0RMKMmXUk8A/ctJzrnHP7nHP3OecaAX2Be82sZ9bqPJosqMcvIdv7egR6DbcTuKRaPltdkQQuFeYmFWicx7r/AslAgnMulsCl56yxdZsJBIasY5hXz6Zc2tmSvdZs22b5TVveuaTz2zCWJbfv5DUClzkTnXOVCYQWy2W7wrgPaAac4bV1dlbZJ9hedrmd5+Y8tv3Nd+Ztm72dODOrlGN9bt89/P47SwXWOueqZHtVcs71gV//vCQRGDpwq5k1yaetzUCCZZttXkAtIiFDQU8kTJhZZTO7CBhJYOzbwly2ucjMmnghZw+QAWT1gvxCYJzU8brWzFqYWXkC478+dYHbr6wAylpgMkU08H9AroP+gQ+Bc83sCjOLMrN4M8u6FFmJQM/RYTPrROAyb5ZPgAvNrKd3jPsIjEebkcsxxgEtzewy73LsncAp2dZ/BNxjZg3NrCKBHrmPXe4zjX8B4nNM1qgE7AX2m1lz4C95nGthVCLQw7fbApNbHjuJtnL6CPg/M6vuTeD4O/BBHtt+AtxpZnXNrCrwaw+wcy6VwPf8lJmVNbM2wE35tJXzz9csYJ+ZPWhm5byJH628X1QgEJQdgbGTzwLvZ+vRzNnWTwR6LR8ws2gz6w5cTODvgkhIU9ATCX1fmtk+Aj0kfwNeIO9bqyQCE4H9wEzgVefc9966pwgEgN1mdv9xHH8EgcHxWwkMyr8TArOAgVsJjNvaRKCHL9d7zznnNhAYh3UfgR7BRfxvTNmtwBPeOf6dQPjI2m85cC3wbwK9iBcTuN3M0VyOsR0YADwN7PC+i+nZNnnbO5cfgLXAYeCOPOpdRiAwrfG+r9rA/QRC6D4CY80+zm3fQnqJwFjD7QQmTkw4ibZy+geQAiwAFgJzvWW5eRP4msAYv7kEJqhkdxWByR+bgc+Bx5xzE/No6y2ghfd9jfF+GbiIwNjCtQTO9T9ArJm1B+4Frve2e4ZA6Hsoj7aOEvjZ9/baedXbd1nhvhKR0itrNp2ISKlgZtcRmDX7lt+1iIiUdOrRE5FSw7tkugE4x+9aRERKAwU9ESlN3gG+JHCrERERKYAu3YqIiIiEKPXoiYiIiIQoBT0RERGREFWoR/eEm2rVqrkGDRr4XYaIiIhIgebMmbPdOZfrDecV9HLRoEEDUlJS/C5DREREpEBmtj6vdbp0KyIiIhKiFPREREREQpSCnoiIiEiIUtATERERCVEKeiIiIiIhSkFPREREJEQp6ImIiIiEKAU9ERERkRCloCciIiISohT0REREREKUgp6IiIhIiFLQExEREQlRCnoiIiIiISrK7wJERETkf5LmJOW5bnD7wcVYiYQC9eiJiIiIhCgFPREREZEQpaAnIiIiEqIU9ERERERClIKeiIiISIhS0BMREREJUQp6IiIiIiFKQU9EREQkRCnoiYiIiIQoPRlDRESkCOX3ZAvQ0y2keKlHT0RERCREKeiJiIiIhCgFPREREZEQpaAnIiIiEqIU9ERERERClIKeiIiISIhS0BMREREJUUENembWy8yWm9kqM3sol/VlzOxjb/1PZtYg27qHveXLzeyCgto0sw+95YvM7G0zi/aWdzezPWb2s/f6ezDPWURERKSkCFrQM7NIYDjQG2gBXGVmLXJsdhOwyznXBHgReMbbtwUwEGgJ9AJeNbPIAtr8EGgOtAbKAYOyHWeqc+507/VE0Z+tiIiISMkTzB69TsAq59wa59xRYCTQL8c2/YD3vPefAj3NzLzlI51zR5xza4FVXnt5tumcG+88wCygbhDPTURERKTEC2bQqwOkZvu80VuW6zbOuXRgDxCfz74Ftuldsr0OmJBtcRczm29mX5lZyxM9IREREZHSJBSfdfsq8INzbqr3eS5Q3zm338z6AGOAxJw7mdlgYDBAvXr1iqtWERERkaAJZo/eJiAh2+e63rJctzGzKCAW2JHPvvm2aWaPAdWBe7OWOef2Ouf2e+/HA9FmVi1nsc65JOdcB+dch+rVqx/fmYqIiIiUQMEMerOBRDNraGYxBCZXJOfYJhm4wXvfH5jkjbFLBgZ6s3IbEuiBm5Vfm2Y2CLgAuMo5l5l1ADM7xRv3h5l1InDOO4JyxiIiIiIlSNAu3Trn0s3sduBrIBJ42zm32MyeAFKcc8nAW8AIM1sF7CQQ3PC2+wRYAqQDtznnMgBya9M75OvAemCml+tGezNs+wN/MbN04BAw0AuTIiIiIiEtqGP0vEul43Ms+3u294eBAXnsOxQYWpg2veW5notzbhgw7LgKFxEREQkBejKGiIiISIhS0BMREREJUQp6IiIiIiFKQU9EREQkRCnoiYiIiIQoBT0RERGREKWgJyIiIhKiFPREREREQpSCnoiIiEiIUtATERERCVEKeiIiIiIhSkFPREREJEQp6ImIiIiEKAU9ERERkRCloCciIiISohT0REREREKUgp6IiIhIiFLQExEREQlRCnoiIiIiIUpBT0RERCREKeiJiIiIhCgFPREREZEQpaAnIiIiEqIU9ERERERClIKeiIiISIhS0BMREREJUQp6IiIiIiFKQU9EREQkRCnoiYiIiISoKL8LEBERCSdJc5L8LkHCiHr0REREREKUgp6IiIhIiFLQExEREQlRCnoiIiIiIUpBT0RERCREKeiJiIiIhCgFPREREZEQpaAnIiIiEqIU9ERERERClIKeiIiISIhS0BMREREJUQp6IiIiIiFKQU9EREQkRCnoiYiIiIQoBT0RERGREKWgJyIiIhKiFPREREREQpSCnoiIiEiIUtATERERCVFBDXpm1svMlpvZKjN7KJf1ZczsY2/9T2bWINu6h73ly83sgoLaNLMPveWLzOxtM4v2lpuZveJtv8DM2gXznEVERERKiqAFPTOLBIYDvYEWwFVm1iLHZjcBu5xzTYAXgWe8fVsAA4GWQC/gVTOLLKDND4HmQGugHDDIW94bSPReg4HXiv5sRUREREqeYPbodQJWOefWOOeOAiOBfjm26Qe8573/FOhpZuYtH+mcO+KcWwus8trLs03n3HjnAWYBdbMd431v1Y9AFTOrFayTFhERESkpghn06gCp2T5v9Jbluo1zLh3YA8Tns2+BbXqXbK8DJhxHHSIiIiIhJxQnY7wK/OCcm3o8O5nZYDNLMbOUtLS0IJUmIiIiUnyCGfQ2AQnZPtf1luW6jZlFAbHAjnz2zbdNM3sMqA7ce5x14JxLcs51cM51qF69eiFOT0RERKRkC2bQmw0kmllDM4shMLkiOcc2ycAN3vv+wCRvjF0yMNCblduQwESKWfm1aWaDgAuAq5xzmTmOcb03+7YzsMc5tyUYJywiIiJSkkQFq2HnXLqZ3Q58DUQCbzvnFpvZE0CKcy4ZeAsYYWargJ0Eghvedp8AS4B04DbnXAZAbm16h3wdWA/MDMznYLRz7glgPNCHwISOg8Afg3XOIiIiIiVJ0IIeBGbCEgha2Zf9Pdv7w8CAPPYdCgwtTJve8lzPxeshvO24ChcREREJAaE4GUNEREREUNATERERCVlBvXQrIiJSGiXNScp3/eD2g4upEpGTox49ERERkRCloCciIiISonTpVkREpAhlukxmb5rN0u1LWbd7HWkH06hRoQa1K9Xm9Jqn06F2B7zbgIkEnYKeiIhIEZm0dhJDfxjKxn0bqRRTiQZVGtCiegvSDqaxeudqUjan8P2677my5ZXUr1Lf73IlDCjoiYiInKSMzAzumnAXw2cPJ75cPH9u92fa12r/m567TJfJzNSZfL7sc56a9hTXnXYdXRO6+li1hAMFPRERkZNw8NhBrvrsKpKXJ3Nv53tpVLUR0ZHRv9suwiLoWq8r7Wq1I2luEiPmjyCCCLokdPGhagkXmowhIiJygvYf3U/P93vy5fIvGdZ7GM9f8HyuIS+7ctHl+EuHv9C8WnPem/8eszbNKqZqJRwp6ImIiJyA9Mx0Bn46kFmbZjFqwChu61T4p23GRMZwa8dbSYxL5P3577N1/9YgVirhTEFPRETkODnnuHvC3YxbOY7hfYZzeYvLj7uNmMgYBrUbRExkDG/Pe5uMzIwgVCrhTkFPRETkOA2fPZzhs4dzf5f7uaXDLSfcTmzZWK5tcy3r96xn/MrxRVihSICCnoiIyHFI3ZPKfd/cx0VNL+KZ85456fba1WpH5zqdGb9qPBv2bCiCCkX+R0FPRESkkI5mHOU/8/5DfLl43un3DhFWNP+MXtnqSspHl2f00tFF0p5IFgU9ERGRQhq1eBS/7P+FEZeOoFr5akXWbvno8vRu0pul25eydPvSImtXREFPRESkEBZvW8wPG37gvEbn0bNRzyJv/w/1/0BcuTjGLB2Dc67I25fwpBsmi4hI2Emak3Rc2x/NOMp/F/2XmhVq0rdZ36DUFB0ZzcVNL+a9+e8xb+s82tVqF5TjSHhRj56IiEgBvlzxJdsPbufaNtcWeEPkk9G5bmdqVaxF8vJkMl1m0I4j4UNBT0REJB+pe1KZuGYiXRO60jS+aVCPFWER9GrSiy37t7A0TWP15OQp6ImIiOTBOcd/F/2XCtEVuPzU478p8onoULsDlctUZtLaScVyPAltCnoiIiJ5mL15Nmt2reHS5pdSIaZCsRwzKiKKP9T/A4vSFunRaHLSFPRERERycST9CJ8t/Yx6sfXoktClWI99dv2ziYqIUq+enDQFPRERkVx8vfprdh/ezZUtryyyGyMXVuUylelYuyM/bvyRg8cOFuuxJbQo6ImIiOSw89BOvln9DR1qd6BJXBNfaujZsCdHMo4wM3WmL8eX0KCgJyIiksPYFWNxOC5rfplvNSTEJlA/tj4zNyroyYlT0BMREclm6/6tzEidwdn1zya+fLyvtXSu25nUvals2rvJ1zqk9FLQExERyeaL5V8QExlD7ya9/S6FTnU6EWER6tWTE6agJyIi4lm/ez1zt8zl3EbnUrlMZb/LoWJMRVrXaM1Pm34iIzPD73KkFFLQExER8YxZPoYK0RU4r9F5fpfyqy51u7D3yF6WbteTMuT4KeiJiIgAy7cvZ0naEno16UW56HJ+l/OrVjVaUSG6Aj9u/NHvUqQUUtATEZGw55zj82WfU6VsFbo36O53Ob8RHRlNx9od+Xnrz+w7ss/vcqSUUdATEZGwt+CXBazdvZYLEy8kJjLG73J+p33t9hzLPMb4leP9LkVKGQU9EREJa5kukzHLx1CjfA26JnT1u5xcNYlrQqWYSny29DO/S5FSRkFPRETCWsrmFDbv20zfZn2JjIj0u5xcRVgEbWu1ZfzK8XokmhwXBT0REQlbmS6T8SvHU7tSbdrXbu93Oflqd0o7Dhw7wNervva7FClFFPRERCRszdkyhy37t3Bh4oVEWMn+J7FpfFPiysXp8q0clyi/CxARETkRSXOS8l0/uP3gfNdn9ebVqliLdrXaFWVpQREZEcmp1U7ls6Wf0WVWF6Ijo3+3TUHnLOGnZP/6IiIiEiTztsxj877N9EnsU+J787K0PaUth9MPs2z7Mr9LkVKidPzJFhERKUKZLpNxK8dxSsVT6FC7g9/lFFrzas0pG1WWn7f+7HcpUkoo6ImISNj5eevPbNq3iT5NSk9vHgRuntyiegsWbVuEc87vcqQUKD1/ukVERIqAc45xK8dRo0KNUtWbl6V1jdbsPrKb1L2pfpcipYCCnoiIhJXk5cls3LuRPol9Sux98/LTqkYrABZuW+hzJVIaKOiJiEjYcM7x+JTHqVG+Bp1qd/K7nBNSuUxlGsQ2YOEvCnpSMAU9EREJG2NXjGXe1nn0TuxdKnvzsrSu2Zp1u9ex78g+v0uREk5BT0REwoJzjid+eIKGVRpyRp0z/C7npLSu0RqHY3HaYr9LkRKuUEHPzEab2YVmpWhqkoiISDZfrfqKlM0p/K3b30p1bx5AQmwClctU1jg9KVBhg9urwNXASjN72syaBbEmERGRIpU1Nq9BlQZcf9r1fpdz0iIsgtY1WrMkbQkZmRl+lyMlWKGCnnNuonPuGqAdsA6YaGYzzOyPZvb7Z7CIiIiUIN+s/oZZm2bxyFmP5ProsNKoVY1WHDx2kLW71/pdipRghb4Ua2bxwI3AIGAe8DKB4PdtPvv0MrPlZrbKzB7KZX0ZM/vYW/+TmTXItu5hb/lyM7ugoDbN7HZvmTOzatmWdzezPWb2s/f6e2HPWURESr+s3rx6sfW44fQb/C6nyDSLb4ZhLE1b6ncpUoIVdoze58BUoDxwsXOur3PuY+fcHUDFPPaJBIYDvYEWwFVm1iLHZjcBu5xzTYAXgWe8fVsAA4GWQC/gVTOLLKDN6cC5wPpcypnqnDvdez1RmHMWEZHQMHHNRGZunMnDZz1MTGSM3+UUmQoxFahfpT5Lti/xuxQpwQrbo/emc66Fc+4p59wWCPTGATjn8rqteCdglXNujXPuKDAS6Jdjm37Ae977T4GeZmbe8pHOuSPOubXAKq+9PNt0zs1zzq0r5PmIiEgYcM4xZMoQ6lauyx9P/6Pf5RS5FtVbsG73Og4eO+h3KVJCFTbo/SOXZTML2KcOkP35LBu9Zblu45xLB/YA8fnsW5g2c9PFzOab2Vdm1jK3DcxssJmlmFlKWlpaIZoUEZGS7ru13zEjdQaPnPUIZaLK+F1OkTu12qlkukyWb1/udylSQkXlt9LMTiEQpMqZWVvAvFWVCVzGLQ3mAvWdc/vNrA8wBkjMuZFzLglIAujQoYOeFC0iUspljc2rW7kuf2r7pyJtO2lOUpG2d6IaVW1EmcgyLN2+lLa12vpdjpRA+QY94AICEzDqAi9kW74PeKSAfTcBCdk+1/WW5bbNRjOLAmKBHQXsW1Cbv+Gc25vt/Xgze9XMqjnnthdQv4iIlGKT1k5i2oZpDOs9LCR78wCiIqJoGt+UJWkapye5yzfoOefeA94zs8udc58dZ9uzgUQza0ggjA0kcC++7JKBGwhcBu4PTHLOOTNLBv5rZi8AtQn0wM0i0KNYUJu/4fVK/uK124nA5eodx3kuIiJSimT15tWpVIeb2t3kdzlB1aJ6CxZuW0jaAQ07kt8r6NLttc65D4AGZnZvzvXOuRdy2S1rXbqZ3Q58DUQCbzvnFpvZE0CKcy4ZeAsYYWargJ0Eghvedp8AS4B04DbnXIZX0+/a9JbfCTwAnAIsMLPxzrlBBALkX8wsHTgEDHTO6dKsiEgIW7FjBVM3TOXfvf9N2aiyfpcTVC2qB24+sXS7brMiv1fQpdsK3n9zvYVKQZxz44HxOZb9Pdv7w8CAPPYdCgwtTJve8leAV3JZPgwYdry1i4hI6fXlii+pXak2g9oN8ruUoKtZoSZVy1ZV0JNcFXTp9g3vv48XTzkiIiInZ/n25azcuZJXer0S8r15AGZGs/hmLE5bjHOOwF3KRAIKe8Pkf5lZZTOLNrPvzCzNzK4NdnEiIiLHa+zKscSWieXP7f/sdynFpml8U/Yd3adePfmdwt5H73xv9upFBJ512wT4a7CKEhERORErdqxgxY4VXND4grDozcvSNL4pAJPXTfa3EClxChv0si7xXgiMcs7tCVI9IiIiJ2zsirFULlOZbvW7+V1KsapWvhpVy1ZV0JPfKWzQG2tmy4D2wHdmVh04HLyyREREjs/KHStZvmM5FzS+IKSeaVsYWeP0Jq+bjG4sIdkVKug55x4CzgQ6OOeOAQf4/XNrRUREfDN2ZaA37+z6Z/tdii+axjcl7WCaxunJbxR0e5XsmhO4n172fd4v4npERESO26qdq1i2fRkDWgwIu968LNnH6WXdW0+ksLNuRwDPAWcBHb1XhyDWJSIiUmjJy5OpFFMpbHvzIDBOL6FygsbpyW8UtkevA9BCT5QQEZGSZtn2ZSzfsZwrWlwRtr15EBin171BdyasmqD76cmvCjsZYxGBR4uJiIiUGM45xiwbQ9WyVcO6Ny9L9wbdNU5PfqOwPXrVgCVmNgs4krXQOdc3KFWJiIgUwqJti1i7ey3XtL6G6Mhov8vxXfcG3QGN05P/KWzQGxLMIkRERI5Xpsvki+VfUL18dbomdPW7nBKhYZWGv47Tu7XjrX6XIyVAYW+vMoXAEzGivfezgblBrEtERCRf87bMI3VvKhc1vYjIiEi/yykRssbp6X56kqWws27/DHwKvOEtqgOMCVZRIiIi+cnIzCB5RTK1KtaiU51OfpdTomicnmRX2MkYtwFdgb0AzrmVQI1gFSUiIpKfDxd+yNb9W+nbrC8RVth/ysJD9nF6IoX923HEOXc064N302T1CYuISLE7mnGUIZOHkFA5gbantPW7nBIn+zg9kcIGvSlm9ghQzszOA0YBXwavLBERkdy9M+8d1u5eS79m/XSvuFxonJ5kV9ig9xCQBiwEbgbGA/8XrKJERERyc+DoAR6f8jhnJpxJqxqt/C6nxNI4PclSqNurOOcyzWwMMMY5lxbkmkRERHL10o8vsWX/FkYNGMXitMV+l1Ni6X56kiXfHj0LGGJm24HlwHIzSzOzvxdPeSIiIgFpB9J4ZvozXNL8ErrW033z8pM1Tu/7dd/7XYr4rKAevXsIzLbt6JxbC2BmjYDXzOwe59yLwS5QREQE4MkfnuTgsYM81fMpv0spsZLmJP36vlalWnyz+hveSHnj17GMg9sP9qs08UlBY/SuA67KCnkAzrk1wLXA9cEsTEREJMuqnat4LeU1BrUbRPNqzf0up1RIjEtk75G9bDuwze9SxEcFBb1o59z2nAu9cXp6qKCIiBSLv036GzGRMTz2h8f8LqXUaBLXBAiEZAlfBQW9oye4TkREpEjM2jSLTxZ/wv1d7qdWpVp+l1Nq1KpYiwrRFVi5c6XfpYiPChqjd5qZ7c1luQFlg1CPiIjIr5xzPPDtA9SoUIP7z7zf73JKFTOjSVwT9eiFuXyDnnNOT4kWERHfjFs5jinrpzC8z3AqlankdzmlTmJcIvN/mc+ewyDVaEoAACAASURBVHuILRvrdzniAz0gUERESqRjGcd4cOKDJMYl8ud2f/a7nFIpa5yeLt+GLwU9EREpkV5LeY0laUt49rxniY7U/L8TUS+2HjGRMQp6YUxBT0RESpztB7fz2OTHOK/RefRt1tfvckqtyIhIGlVtpHF6YUxBT0RESpxHJz3KviP7eKnXS7/e7FdOTJO4Jmzau4mDxw76XYr4oFDPuhURESku87fOJ2luErd3vJ1pG6YxbcM0v0sq1RLjEnE4Vu9c7Xcp4gP16ImISInhnOOuCXdRtWxVhnQf4nc5IaFR1UZEWIQu34Yp9eiJiEiJ8dnSz5iyfgqvXfgaVctV9buckBATGUP92PqakBGm1KMnIiIlwqFjh7j/m/tpU7ONbqdSxJrENWH9nvUcTj/sdylSzBT0RESkRHhuxnOs37Oel3u9TGSE7tdflBLjEknPTGfWpll+lyLFTJduRUTEd2t2reGpaU/Rv0V/ujfoXiRtJs1JKpJ2QkHjuMYATF0/lbPrn+1zNVKc1KMnIiK+cs5x67hbiYyI5MULXvS7nJBUMaYitSvVZuqGqX6XIsVMPXoiIuKrkYtG8vXqr3m518vUrVzX73JCVpO4JsxInUFGZoYujYcR9eiJiIhvdh3axd1f303H2h25reNtfpcT0hLjEtl3dB/zf5nvdylSjBT0RETENw9OfJAdB3eQdHGSepmCrElcEyAwTk/Chy7dioiIL6ZtmMabc9/kvEbnMWvTLM0IDbK4cnHUj63P1A1TuavzXX6XI8VEPXoiIlLsjmYc5eaxNxNXLo6Lm17sdzlho1v9bkzdMBXnnN+lSDFR0BMRkWL37PRnWZK2hKtbXU2ZqDJ+lxM2utXrxrYD2/SUjDCioCciIsVqSdoSnvzhSfq36E/rmq39LiesdKvXDdA4vXCioCciIsXmaMZRrh19LZXLVGZY72F+lxN2mldrTrXy1XQ/vTCiyRgiIlJsnpzyJPO2zmP0FaOpWbGm3+WEHTPjrHpnKeiFEfXoiYhIsfhx44/8c9o/ueG0G7j01Ev9LidsdavXjTW71rB532a/S5FioKAnIiJBd+DoAa7//HrqVq7Ly71e9rucsKZxeuFFQU9ERILuwYkPsnLnSt7t9y6xZWP9Liesta3VlgrRFXT5NkwENeiZWS8zW25mq8zsoVzWlzGzj731P5lZg2zrHvaWLzezCwpq08xu95Y5M6uWbbmZ2SveugVm1i54ZywiIjl9s/obhs8ezj2d7+Gchuf4XU7Yi4qIoktCFwW9MBG0oGdmkcBwoDfQArjKzFrk2OwmYJdzrgnwIvCMt28LYCDQEugFvGpmkQW0OR04F1if4xi9gUTvNRh4rSjPU0RE8rbj4A7+9MWfOLXaqQztMdTvcsTTrV43Fv6ykN2Hd/tdigRZMGfddgJWOefWAJjZSKAfsCTbNv2AId77T4FhZmbe8pHOuSPAWjNb5bVHXm065+Z5y3LW0Q943wVuA/6jmVUxs1rOuS1FerYiIqVU0pykfNcPbj/4hNrNdJncMOYG0g6mkXxVMuWiy51QO1L0utXrhsMxfcN0Lmx6od/lSBAF89JtHSA12+eN3rJct3HOpQN7gPh89i1MmydSB2Y22MxSzCwlLS2tgCZFRKQgL8x8gXErx/Hcec/RrpZGzZQkZ9Q9g+iIaF2+DQOajOFxziU55zo45zpUr17d73JEREq1Hzf+yMPfPcxlp17G7Z1u97scyaF8dHna126voBcGghn0NgEJ2T7X9Zbluo2ZRQGxwI589i1MmydSh4iIFJFtB7YxYNQA6lauy1t938ptSI2UAN3qdWP2ptkcOnbI71IkiIIZ9GYDiWbW0MxiCEyuSM6xTTJwg/e+PzDJG0uXDAz0ZuU2JDCRYlYh28wpGbjem33bGdij8XkiIsGRnpnOFaOuYPvB7Yy+YjRVylbxuyTJQ7d63TiWeYxZm2b5XYoEUdCCnjfm7nbga2Ap8IlzbrGZPWFmfb3N3gLivckW9wIPefsuBj4hMHFjAnCbcy4jrzYBzOxOM9tIoMdugZn9xzvGeGANsAp4E7g1WOcsIhLuHvj2Aaasn0LSRUm0rdXW73IkH13rdQXQ5dsQF9Rn3TrnxhMIWtmX/T3b+8PAgDz2HQr8bi5+bm16y18BXslluQNuO97aRUTk+Lw//31e/PFF7uh0B9eddp3f5UgB4srF0apGKwW9EKfJGCIictKmbZjGoORB9GjYg+fPf97vcqSQutXrxozUGaRnpvtdigSJgp6IiJyUNbvWcOnHl9KgSgNGDRhFdGS03yVJIXWr1439R/czf+t8v0uRIFHQExGRE7bz0E4u/O+FZGRmMPbqscSVi/O7JDkO3ep3AzROL5QFdYyeiIiErkPHDnHxRxezaucq7ux0J5PXTWbyusm/2eZEn6ohwZHbU1Diy8Xz/vz3ubvz3T5UJMGmHj0RETluGZkZXD36amamzuRPp/+JZtWa+V2SnKDEuERW7VxFYO6ihBoFPREROS6ZLpNBXw5izLIxvNTrJdrXbu93SXISmsQ1Yd/RfazYscLvUiQIFPRERKTQMl0mt4y9hXd/fpfHuz/OnWfc6XdJcpIS4xMBjdMLVQp6IiJSKM457vzqTt6c+yaPnPUIj579qN8lSRGoWaEmlWIqKeiFKE3GEBEJAbkNss/uZCdFOOe4/5v7GT57OPd1uY9/9PhHoZ5hW1Bd4j8zo3FcY6auV9ALRerRExGRfDnneOS7R3jhxxe4o9MdPHves4UKeVJ6JMYlsnb3Wjbt3eR3KVLEFPRERCRPzjkemvgQT09/mpvb38zLvV5WyAtBTeKaABqnF4oU9EREJFeZLpMPFnzAv2b8i790+AuvXviqQl6ISqicQMWYirp8G4I0Rk9ERH7nWMYx3pr3FvO2zuPRsx/l8e6PK+SFsMiISLrU7aIevRCkHj0REfmNw+mHGTZ7GPO2zuOKFlfwxDlPKOSFgW71urFo2yJ2HdrldylShBT0RETkV/uP7ufFmS+yYscK/nj6H+nZqKffJUkx6Va/Gw7H9NTpfpciRUhBT0REANi6fytPT3uaTfs2cUuHW+hct7PfJUkxOqPOGcRExjBl3RS/S5EipDF6IiLC8u3LeX3O60RaJPd0uYfGVRv7XZIUs3LR5ehctzPfr/ve71KkCKlHT0QkzM1IncHLP71MbJlYHjrrIYW8MHZOg3OYt3Ueuw/v9rsUKSLq0RORkBPsp0SEikyXyaOTHuW9+e9xarVTGdx+MOWjy/tdlvjonAbn8PiUx/lh/Q/0bdbX73KkCKhHT0QkDO0/up8rRl3BP6f9k7PqncUdne5QyBM61+1M2aiyfL9Wl29DhXr0RETCzKqdq7hk5CUs3b6U589/ngrRFXT7FAGgTFQZzkw4U+P0Qoh69EREwsiEVRPo+GZHtuzfwtfXfs29Xe5VyJPf6F6/O/N/mc+Ogzv8LkWKgIKeiEgYcM7x9LSn6fNhH+rH1iflzymc2+hcv8uSEuichucAMGW9brMSChT0RERC3JH0Iwz8bCAPf/cwV7S8gul/mk7Dqg39LktKqE51OlE+urzG6YUIjdETEQlhaQfSeC3lNbbs38K/zv0X9595vy7VSr5iImPomtBV4/RChHr0RERC1KJti/jntH+y6/AuvrrmK/7a9a8KeVIoPRr2YHHaYrbu3+p3KXKSFPREREJMpsskeXkyw2YNI65sHI+c9QjnNz7f77KkFMkavzlp7SSfK5GTpaAnIhJC9h3Zxys/vcK4lePoktCFB896kOoVqvtdlpQybU9pS9WyVfl2zbd+lyInSWP0RERCxOqdq0mam8SBowe4vs31dK3XtUjaLehJIxJ6IiMi6dmoJxPXTMQ5p0v+pZh69ERESjnnHBPXTOS5mc8RHRHNg10fLLKQJ+Hr3IbnsnHvRlbsWOF3KXISFPREREqxvUf2csWnVzBqySja1GjDI90eISE2we+yJARkjdObuGaiz5XIyVDQExEppeZsnkOHpA58vvRzLj/1cm7pcIueVytFplHVRjSo0kDj9Eo5BT0RkVLGOceLM1+ky1tdOJR+iO9v+J7zG5+vcVRSpMyM8xqdx/frvic9M93vcuQEaTKGiEgpknYgjRu/uJHxK8fTr1k/3ur7FvHl41m6fWm++2lChZyIcxudy5tz3yRlcwqd63b2uxw5AerRExEpJSatncRpr5/Gd2u+Y1jvYXx+5efEl4/3uywJYT0a9gDg29W6fFtaKeiJiJRwB48d5K6v7qLn+z2JLRvLT4N+4rZOt+lSrQRdtfLVaF+rPV+v/trvUuQEKeiJiJRgM1JncPrrp/PKrFe4o9MdpPw5hdNOOc3vsiSM9G7Sm5kbZ7Lr0C6/S5EToKAnIlICHU4/zAPfPkC3d7pxLPMYk66fxCu9X6FCTAW/S5Mw06tJLzJdpm6zUkop6ImIlDCT102m3RvteHbGswxqO4gFtyzgnIbn+F2WhKkz6p5BlbJV+GrVV36XIidAs25FRIpJQTNf+yT24a/f/pWRi0bSoEoDJlwzgQuaXFBM1Um4y+/PZ+OqjZmwaoIeh1YKqUdPRMRnxzKOMWHVBJoPa86YZWMY8ochLLl1iUKelBgta7Rky/4tLNy20O9S5DipR09ExCeZLpN5W+YxZvkYth3YxiXNL+GF81+gYdWGfpcm8hstq7cE4KuVX9GmZhufq5HjoR49EZFilhXwhk4dStLcJCItkjs63cHnV36ukCclUpWyVTit5mlMWD3B71LkOKlHT0SkmBxJP8JPm35i4pqJ/HLgF6qXr84fT/8jnep0IsL0e7eUbL2b9Oa5mc+x98heKpep7Hc5UkgKeiISFAVNPBjcfnDQ2j4ZRV23c47Zm2fz9ry3eW/+exxOP0y92HoMajeIdqe0IzIi8mTKFSk2fRL78PT0p/lm9Tf0b9Hf73KkkBT0RESK2MFjB5m2YRoTVk3gs6WfsWHPBspFleP0U07nrISzaBLXRDMXpdTpktCF+HLxJC9PVtArRRT0REROws5DO1m9czXLti9j7pa5pGxJYdamWRzNOEpMZAznNz6fJ7o/Qb/m/fhk8Sd+lytywqIioriw6YWMXTGW9Mx0oiIUIUoD/ZREpNTLyMwg7WAaW/dvZfvB7ew8tJO9R/ZyKP0Qh9MP//o6mnEUw3jyhyeJsAgiLZLIiEjKRJahTFQZykaVZcfBHURFRBEdEU1UZBSGkeEyyMgMvEYtGcX+o/tJO5DGtgPb2Hd03691ZPXa3dnpTno07EG3+t2oGFPRx29GpGj1bdqX9+e/z4zUGZxd/2y/y5FCUNATkVJn9+HdLN2+lLW71rJ291o279tMemb6r+tjImOoUqYKZaPLUjaqLPHl4ikbVZboyGgAmsY1JZPMQHhzGRxJP8KRjCMcTj/MNreNI0ePkJ6ZzrHMYzjniIwIBMJIi6TS0UpUiK5AgzoNqF6+OvVj69M4rjFN45vSLL6ZxtxJSDu/8fnERMaQvDxZQa+UCGrQM7NewMtAJPAf59zTOdaXAd4H2gM7gCudc+u8dQ8DNwEZwJ3Oua/za9PMGgIjgXhgDnCdc+6omd0IPAts8g47zDn3n2Cds4gUvWMZx5ieOp0Jqybw1aqvWPDLAgDKRpWlfmx9ejToQe1KtalVqRbVy1enfHT5fMfA5Teh4mQnY5zMRJFgTjIRKQqVylTinAbn8MXyL3j2vGc11rQUCFrQM7NIYDhwHrARmG1myc65Jdk2uwnY5ZxrYmYDgWeAK82sBTAQaAnUBiaaWVNvn7zafAZ40Tk30sxe99p+zdvnY+fc7cE6VxEpepkuk8nrJjNiwQhGLx3N3iN7iYqI4qx6Z3FZ88toUaMFdSrV0W1JRIpZ32Z9uW38bSzfsZzm1Zr7XY4UIJj/h+wErHLOrXHOHSXQ29Yvxzb9gPe8958CPS3w60E/YKRz7ohzbi2wymsv1za9fXp4beC1eUkQz01EgmTxtsU8NPEh6r9Un57v92T00tFcfurljLlyDDsf2Mn3N3zPBU0uIKFygkKeiA8ubnoxAMnLk32uRAojmJdu6wCp2T5vBM7IaxvnXLqZ7SFw6bUO8GOOfet473NrMx7Y7ZxLz2V7gMvN7GxgBXCPcy57GwCY2WBgMEC9evUKeYoiUhSOZhxl9NLR/HvWv5mROoOoiCh6NenF8+c/z8VNL6ZcdDm/SxQRT0JsAm1PacvopaN5oOsDfpcjBQiHyRhfAh85546Y2c0Eevt65NzIOZcEJAF06NDBFW+JIuFpy74tJM1J4vU5r7N1/1aaxDXhhfNf4Jo211CjQg2/yxORPPRv0Z+/TfobqXtSSYhN8LscyUcwg94mIPtPvy7/mxCRc5uNZhYFxBKYlJHfvrkt3wFUMbMor1fv1+2dczuybf8f4F8ncU4iUgQ27d1El7e6MGvTLDJdJq2qt2JAiwG0qN6CCItgzLIxJ/XkjIJo0oPIyckKep8t/Yy7O9/tdzmSj2AGvdlAojcbdhOByRVX59gmGbgBmAn0ByY555yZJQP/NbMXCEzGSARmAZZbm94+33ttjPTa/ALAzGo557Z4x+sLLA3WCYtI3pxzrNq5iq9Xf83CbQuJiYyhe/3unNPwHPXeiZQyTeOb0qZmGz5d8qmCXgkXtKDnjbm7HfiawK1Q3nbOLTazJ4AU51wy8BYwwsxWATsJBDe87T4BlgDpwG3OuQyA3Nr0DvkgMNLM/gHM89oGuNPM+nrt7ARuDNY5i8jvOedYuG0hX636ijW71lAxpiIXN72Y7g2662bCIqXYgBYDePT7R9m0dxN1KtcpeAfxRVDH6DnnxgPjcyz7e7b3h4EBeew7FBhamDa95WsIzMrNufxh4OHjrV1ETo5zjvm/zGfcynFs2LOB+HLxDGw5kK71uhITGeN3eSJykvq36M+j3z/K6KWjueOMO/wuR/IQDpMxRKQYZbpMvlj2BUOnDiV1byrVy1fn+tOup3OdznpqhEgIaV6tOa1qtGLUklEKeiWYgp5IiDuZiQfHMyEi02UyeulonvzhSRb8soAa5Wtw42k30qlOpxMKeJowIVLy9T+1P49PeZwt+7ZQq1Itv8uRXOhuoyJyUjJdJp8s/oTTXj+NAaMGcDj9MCMuHcGQ7kPoktBFvXgiIezKVlficIxcNNLvUiQPCnoickIyXSYfL/qY1q+15spPryQ9M50PL/uQJbcu4do21yrgiYSB5tWa06F2Bz5Y+IHfpUgeFPRE5Lg450henkzbN9oy8LOBAHx0+Ucs+ssirm59tQKeSJi5tvW1zN0ylyVpSwreWIqdgp6IFIpzjm9Xf0vntzrTb2Q/Dh47yIeXfciCWxYwsNVABTyRMDWw1UAiLZIR80f4XYrkQpMxRKRA0zZM4/8m/R9T1k+hXmw9/nPxf7j+tOt55+d3eGveWwU3ICIhq2bFmpzf+Hw+XPghQ3sOJcLUh1SS6KchInlK2ZxC7w970+2dbizfsZx/9/43K25fwU3tbiI6Mtrv8kSkhLiuzXWk7k3lh/U/+F2K5KAePRH5nc37NvPF8i/4eevPxJWL41/n/ovbOt1G+ejyfpcmIiVQv+b9qBhTkRHzR9C9QXe/y5FsFPRE5Fe7Du0ieUUyM1NnUiaqDI93f5y7O99N5TKV/S5NREqw8tHluaLFFXy8+GNe7PWi/p9RgijoiQiHjh1iwuoJfLfmOxyOcxudS68mvbi3y71+lyYipcTg9oN5++e3+WjhR9zc4Wa/yxGPgp5IGEvPTOeH9T8wdsVYDhw7QKc6nejXrB/VylfzuzQRKWU61elEm5pteGPOGwxuPxgz87skQUFPJCw555izZQ6fL/uc7Qe30yy+Gf1b9KdebD2/SxORUsrMuLn9zdw2/jZSNqfQsU5Hv0sSFPREws6KHSv4bMlnrNuzjjqV6nBHpztoWb2lfvsWkZN2Tetr+Ou3f+WNOW8o6JUQCnoiYWLzvs2MXjqahdsWUrVsVW487UbOqHuG7nklIkUmtmwsV7W6io8WfcQLF7ygSRklgIKeSIjbfXg3Xy7/kump0ykTVYZLm19Kj4Y9iImM8bs0EQlBt3S4hbfmvcW7P7/LnWfc6Xc5YU9BT6QESJqTlO/6we0HH3ebe4/s5dnpz/LM9GfIdJn0aNiDPol9qBhT8UTLFBEpUIfaHeia0JWXfnyJ2zrepscj+kxBTyTEHM04StKcJJ6Y8gRpB9PoWLsj/Zr1o3qF6n6XJiJh4r4u93HZJ5fx+bLP6d+iv9/lhDUNzhEJEc45Pl3yKS1fbckdX91Byxotmf3n2QxqN0ghT0SKVd9mfWlctTHPz3ze71LCnoKeSAiYun4qZ759JgNGDaBMZBnGXT2OSddPokPtDn6XJiJhKDIikns638OPG39kRuoMv8sJawp6IqXYkrQl9BvZj7PfPZsNezbwVt+3mH/LfPok9tHtUkTEVzeefiNVy1bl2RnP+l1KWNMYPZFSaOPejQyZPIR3fn6HijEVGdpjKHd3vpvy0eX9Li3kFTRxRkQCKsRU4PZOt/PkD0+y4JcFtKnZxu+SwpJ69ERKkd2Hd/PQxIdI/HciIxaM4K4z7mL1nat5pNsjCnkiUuLc0/keYsvEMmTyEL9LCVvq0RMpBQ6nH2b4rOEMnTqU3Yd3c02ba3jynCdpUKWB36WJiOSparmq3NvlXh6b/Bhzt8ylXa12fpcUdtSjJ1KCZbpMZm6cSbNhzbj/2/vpVKcTc2+ey4hLRyjkiUipcNcZd1G1bFUem/yY36WEJfXoiZRAzjkWblvIF8u+YOO+jbSv1Z63+75Nz0Y9/S5NROS4xJaN5f4z7+dvk/7GTxt/4oy6Z/hdUlhR0BMpQZxzLElbQvKKZNbtXkf18tUZ1G4Q7Wu1Z/Wu1ayes/p3+5zIUzMKSxMPRKQo3NHpDl756RXu+foepv9puu4KUIwU9ERKiGXbl5G8PJnVu1YTXy6e69pcR5e6XfT4IBEp9SqVqcRTPZ/iT8l/4sOFH3Jtm2v9LilsKOiJ+Gzq+qk8P/N5VuxYQZWyVbi61dV0rdeVqAj99RSR0HHD6TfwWsprPDjxQS5pfomeu11MNBlDxAfOOaasm8L5I87n7HfPZuv+rVzZ8kr+cc4/+EODPyjkiUjIibAIXun9Cpv3beafU//pdzlhQ/+aiBSjTJfJ2BVjeXra08zcOJPq5avz3HnPUSaqDDGRMX6XJyISVJ3rdua6Ntfx3IznuKrVVbSu2drvkkKeOef8rqHE6dChg0tJSfG7DClhCpqYkN+kiGMZxxi5aCTPTH+GxWmLaVClAX8986/88fQ/Ui66nCY9iEiJV1QTv9IOpNHy1ZbUrVyXnwb9RHRkdJG0G87MbI5zLteHm+vSrUgQHTx2kGGzhpH470SuH3M9ZsYHl37AyjtWcmvHWykXXc7vEkVEilX1CtV546I3mLd1Hk9Ne8rvckKeLt2KBEHqnlReS3mNpDlJ7Di0gzMTzmRYn2H0SexDhOn3KxEJb5eeeilXt76aJ394kouaXqQnZgSRgp5IEXHOMT11Oq/89Aqjl47G4ejXrB/3dL6HbvW7+V2eiEiJ8u/e/2byuslcMeoKUganUKVsFb9LCkkKeiIn6cDRA8zaNItXZ7/K/F/mU7VsVe7rch+3dryV+lXq+12eiEiJFFcujlEDRvGHd//AdZ9fxxcDv9AVjyBQ0BM5AZkukxU7VjB9w3Tmbp1LemY67Wq1442L3uDaNtdSPrq83yWKiJR4ZyacyUsXvMTtX93Ok1Oe5LHueh5uUVPQEykk5xwb920kZXMKKZtT2H5wO+Wjy3NWwll0rdeVerH1APhgwQe/2zeYjykTESnNbu14K7M2z2LIlCE0jmusp2YUMQU9kXw451i2fRmfLP6E1+e8ztb9W4mwCJpXa07fpn1pW6ut7n8nInISzIw3LnqDjXs3cuOYG4ktE8vFzS72u6yQoaAnksOR9CNMWT+FcSvGMW7lOFbvWo1hJMYn0qNhD9qd0o5KZSr5XaaISMgoG1WWMVeOoef7PRkwagDjrxlPj4Y9/C4rJCjoSdjLyMxgwS8LmLJ+Ct+v+57v1nzHgWMHKBtVlh4Ne3Bvl3u5pPkljF0x1u9SRURCVqUylRh/zXi6v9udPh/24b+X/5fLTr3M77JKPQU9CTt7j+xl3pZ5/LTpJ35Y/wPTNkxjz5E9ADSs0pDrT7ueCxMv5JyG52hShYhIMapWvhpTbpzCRR9dRP9P+jO8z3D+0vEvfpdVqinoSbE72cd9FXZiQ0ZmBql7U1mxYwWLty1mzpY5TFwzkW0HtuEIPPqvZoWatKnZhsS4RBLjE/+/vXsPzqq+8zj+/uRiIIEEEkhAtuEemLiKiq3s4G3ACzC1WJcCuqOpdXqzna62zm4cx1l1nKnuzW7V0e5W69Kh1VZll3Uv3qq24wzKXVAqV1EpEq7hJoGE7/5xfk88hOcJgTxPnjxPvi/mzDnP7znnd8758fud55tz+9F4SWO3ti0V7+LMOZfPutNFZEdVpVW8dvNrzHtuHrf9z22s+nQVP5nxE+9J6Ax5oOdympnRdKiJ9bvXs373ejbs2dA+vXHPRlraWtrnHTFwBMMGDOPiERczctBIaitqKS8pz+LWO+ecS6a0uJRF8xZxz+/u4cG3HmTJtiU8O+dZJg6ZmO1Nyzke6Lmc8Nmxz2g61MSOQzvYfmA76/esbw/o9rfsb5+vuKCYcZXjqKuqY9b4WdRV1VFXVceEqgnUDKjxM2vOOZcjigqK+PGVP+aykZdx06KbmPTEJBqnNnLXpXfRr6hftjcvZ3ig53qNY23H2Hl4ZxTQHdzBjkPR0HSo6YRgToiRg0ZSV1XHzefd3B7M1VXVUVtRS2FBYRb3wjnnXDrNHD+Ttbet5Ucv/4j7f38/C9cs5IFpD/C1+q/58b4LPNBzPar1eCs78G8kPAAADDhJREFUD0XBXOIMXSKY2314d/u9cwDlJeVUl1VzbvW5VJdVU1NWQ82AGhovafS/5pxzrg8ZNmAYC69fyC3n38Lt/3c7Nzx/A/e/eT+NlzQy95y5/pvQCQ/0XNq1Hm9l676tbNyzkQ17Npww3rJ3C8eOH2uft19RP6rLqhk9aDRTRkyhZkBNe1CX6sbbBasX9NSuOOec60WuHHMl7373XZ57/znue/M+Gv6jgTteuoOGSQ3ceO6NTB4+GUnZ3sxeJaOBnqQZwL8AhcDPzezBDt+XAAuAycBuYJ6ZfRi+uwu4FWgDfmBmL3WWp6TRwDNAFbAcuMnMjna2DndmzIx9R/axtXkrHzV/dFJQt2XfFlqPt7bPX1ZcxrjKcZxXcx7XT7yePx34E0PLhlJTVkN5Sbk3Suecc11WoALmnjOXOfVzeH3L6/xs+c945J1HeHjJw9RW1HJt3bVMGz2Ny0deTlVpVbY3N+syFuhJKgQeA64CPgGWSlpsZu/HZrsV2Gtm4yTNBx4C5kmqB+YD5wBnA69KqgvLpMrzIeBhM3tG0hMh78dTrSNT+53rDh492H6PXPul1YM72HZgW3tg91HzRxw8evCE5cqKyxhfNZ5JwyYxp34O4yrHMb5yPOMqxzFswLATgjl/IMI551x3FaiA6WOmM33MdHYf3s2L61/k+XXP84tVv+CxpY+192g0efhkJtVMYmzlWMYOHsvYyrF96o0LmTyj9yVgo5ltBpD0DDAbiAd6s4F7w/RzwKOKIoLZwDNm1gJskbQx5EeyPCWtA6YBN4Z5/j3k+3iqdZjZ5zeD9RJmxnE7nnJos7aT04630dLWwpHWI7S0hnGHz4nhwNEDNB9pZn/LfppbmmluCdNHmtl7ZC9Nh5o4fOxw0m0bUjqE2opaJlRN4KoxV1FbUUttRS0jK6LXlFSXVfuZOeecc1lRVVpFw/kNNJzfwNG2oyzdtpQ3PnyDZduX8dbHb/Hrtb8+cf7+VYwaNIrqsmqGlA5pHyr7V1JWXEZpcSn9i/tH46L+7Z9LCksoLCikUIUUFhRSoIL26fi4QAW95jcxk4HeCODj2OdPgItTzWNmrZKaiS69jgCWdFh2RJhOlmcVsM/MWpPMn2odu854z7pp9aermfLklJOCtp5QqELKS8qp6FcRjUsqOHvg2dQPrW9/2CFxj1x1WTU1A2oYWjqUkqKSHtk+55xzrjvOKjyLqbVTmVo7tT2t+UgzW/ZtYdOeTWzau4nNezeztXkrOw/vZN2udew6vOukK1XdJURhQSHfmfwdHpn1SFrzPh3+MEYg6VtA4tXdByV9cIZZDSGLQeSptNHG3vCvh/Xqckn4Nt/u6VXmRLlkgZdLcl4uyXm5JJfWcunO8TELx9ZTyXidMYxWWnk0/Muwkam+yGSgtw34Quzzn4W0ZPN8IqkIqCB6YKKzZZOl7wYGSSoKZ/Xi86daxwnM7F+Bbt88JmmZmV3U3XzyjZdLcl4uyXm5JOflkpyXS3JeLqn1pbIpyGDeS4HxkkZLOovo4YrFHeZZDDSE6TnA78K9c4uB+ZJKwtO044F3UuUZlnk95EHI8z9PsQ7nnHPOubyWsTN64X647wMvEb0K5Skze0/S/cAyM1sMPAn8MjxssYcocCPM9xuiBzdage+ZWRtAsjzDKv8WeEbSA8DKkDep1uGcc845l+/kJ7fSS9K3wmVgF+PlkpyXS3JeLsl5uSTn5ZKcl0tqfalsPNBzzjnnnMtTmbxHzznnnHPOZZEHeqdB0j9I+qOkdyUtkjQopI+S9JmkVWF4IrbMZElrJG2U9NPwQmgkVUp6RdKGMB6crf3KJEkzJH0Q9r8x29uTaZK+IOl1Se9Lek/SX4f0eyVti9WRWbFl7grl84Gka2LpeVV2kj4MbWGVpGUhLWk7UOSnYd/flXRhLJ+GMP8GSQ2p1pcLJE2I1YlVkvZLur2v1hdJT0lqkrQ2lpa2OpLqeNzbpSiXPv97lKJc0tZ2FD34+XZIf1bRQ6C5x8x86OIAXA0UhemHgIfC9ChgbYpl3gGmAAL+F5gZ0v8eaAzTjYm88mkgemBmEzAGOAtYDdRne7syvM/DgQvD9EBgPVBP1DvLnUnmrw/lUgKMDuVVmI9lB3wIDOmQlrQdALNCe1FoP2+H9EpgcxgPDtODs71vaSqfQuBTovdh9cn6AlwGXBg/nqazjqQ6Hvf2IUW59PnfoxTlkra2A/wGmB+mnwC+m+19PpPBz+idBjN72T7vfWMJ0fv6UpI0HCg3syUW1ZQFwHXh69lEXbURxtclySLXtXeDZ2ZHgUQ3eHnLzLab2YowfQBYx+e9tCTT3t2fmW0BEt399ZWyS9UOZgMLLLKE6D2Zw4FrgFfMbI+Z7QVeAWb09EZnyHRgk5lt7WSevK4vZvZ7orcjxKWljpzieNyrJSsX/z1KWV9SOa22E852TiPqOhVyqFw68kDvzH2D6C+ihNGSVkp6U9KlIW0EUXdsCfGu2WrMbHuY/hSoyejWZkeybvA6C3ryiqRRwAXA2yHp++Eyy1OxSyOpyigfy86AlyUtV9QTDaRuB32pXBLmA/EOOft6fUlIVx3p7Hic6/z36ETpaDudda2aUzzQ60DSq5LWJhlmx+a5m+j9fgtD0nag1swuAH4I/EpSeVfXGf668sef84ikAcDzwO1mth94HBgLnE9UX/4pi5uXLZeY2YXATOB7ki6Lf9mX20G49+crwG9DkteXJPpyHUnFf49O4m2nA+/rtgMzu7Kz7yV9HfgyMD00CMysBWgJ08slbQLqiLpfi59Oj3fNtkPScDPbHk6pN6V1R3qHrnSDl3ckFRMFeQvN7AUAM9sR+/7fgBfDx9Pt7i9nmdm2MG6StIjokkmqdpCqXLYBV3RIfyPDm94TZgIrEvXE68sJ0lVHOjse5yT/PTpZGttOZ12r5hQ/o3caJM0A/gb4ipkdjqUPlVQYpscQddm2OZwK3y9pSrjefzPJu2aLd9mWT7rSDV5eCf/PTwLrzOyfY+nDY7N9FUg8JXZa3f31xD5kgqQySQMT00Q3kq8ldTtYDNysyBSgObSnl4CrJQ0Ol2SuDmm57gZil237en3pIC115BTH45zjv0fJpavthMA5VdequSXbT4Pk0kB08+bHwKowPBHS/xJ4L6StAK6NLXMRUUXbBDzK5y+prgJeAzYArwKV2d6/DJXZLKInTzcBd2d7e3pgfy8huuzxbqyezAJ+CawJ6YuB4bFl7g7l8wGxpwDzqeyInmhbHYb3EvuTqh0QPRX4WNj3NcBFsby+EdriRuCWbO9bGsqmjOjsQUUsrU/WF6JgdztwjOieqFvTWUdSHY97+5CiXPr871GKcklb2wnHrXdCWf8WKMn2Pp/J4D1jOOecc87lKb9065xzzjmXpzzQc84555zLUx7oOeecc87lKQ/0nHPOOefylAd6zjnnnHN5yl+Y7JxzHUhKvG4CYBjQBuwMn79kUZ+Ymd6Ge4GDZvaPmV6Xcy5/eaDnnHMdmNluoi6UPOByzuU0v3TrnHNdIOmbkpZKWi3peUmlIf1pSY9LWiJps6QrQmfq6yQ9HVv+cUnLJL0n6b5Y+oeS7pO0QtIaSRNjq62X9EbI9wexZX4Y64f79p7Yf+dcbvJAzznnuuYFM/uimU0C1hG9hT9hMPAXwB1Eb+N/GDgHOFfS+WGeu83sIuA84HJJ58WW32VmFxJ1yH5nLH0icA1Rv8B/J6lY0mTgFuBiYArwTUkXpHlfnXN5wgM955zrmj+X9AdJa4C/IgrkEv7Lom6G1gA7zGyNmR0n6opqVJhnrqQVwMqwbH1s+RfCeHlsfoD/NrMWM9tF1NF8DVE3e4vM7JCZHQzLXprG/XTO5RG/R88557rmaeA6M1st6evAFbHvWsL4eGw68bkodKJ+J/BFM9sbLun2S7J8Gycel+N5dfzOOedOyc/oOedc1wwEtksqJjqjdzrKgUNAs6QaYGY3tuMPwHWSSiWVAV8Nac45dxL/69A557rmHuBtotesvE0U+HVJOAu4Evgj8DHw1pluhJmtCGcE3wlJPzezlWean3Muvym6rcQ555xzzuUbv3TrnHPOOZenPNBzzjnnnMtTHug555xzzuUpD/Scc8455/KUB3rOOeecc3nKAz3nnHPOuTzlgZ5zzjnnXJ7yQM8555xzLk/9P4WOrgYYI9QmAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "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": 323, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qu-5y7LFvou_", + "outputId": "b1852644-0ece-45d7-e1e5-6c663390526f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total: 834\n", + "Total de sim: 98\n", + "Total de não: 735\n" + ] + } + ], + "source": [ + "#total de sim e não\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": "markdown", + "source": [ + "

Problema 1

" + ], + "metadata": { + "id": "5Jz_fMKBgw37" + } + }, + { + "cell_type": "markdown", + "source": [ + "Nos dados disponíveis existem um desbalanceamento das classes Comportamento agressivo? (sim ou não), onde a classe 'não' é a majoritória e a classe 'sim' a minotória. Isso induzirá o algoritmo de classificação a tender mais para a classe majoritária prejudicando na acurácia do modolo.\n", + "\n", + "Existem métodos para tratamento de classes desbanlanceadas como o **Undersampling** e o **Oversampling**.\n", + "\n", + "* **Undersampling**: é uma técnica que consiste em manter todos os dados da classe com menor frequência (minoritária) e diminuir a quantidade dos que estão na classe de maior frequência (majoritária), fazendo com que as observações no conjunto possuam dados com a variável alvo equilibrada. Uma das técnicas mais utilizadas é o Near Miss que diminui aleatoriamente a quantidade de valores da classe majoritária. Vale destacar que o Near Miss utiliza a menor distância média dos K-vizinhos mais próximos, ou seja, seleciona os valores baseando-se no método KNN (K-nearest neighbors) para reduzir a perda de informação.\n", + "* **Oversampling**: é uma técnica que consiste em aumentar a quantidade de registros da classe com menor frequência até que a base de dados possua uma quantidade equilibrada entre as classes da variável alvo. Para evitar que existam muitos dados idênticos, pode ser utilizada a técnica SMOTE (Synthetic Minority Over-sampling Technique), que consiste em sintetizar novas informações com base nas já existentes. Esses dados “sintéticos” são relativamente próximos aos dados reais, mas não são idênticos. \n", + "\n", + "**Referências:**\n", + "https://www.alura.com.br/artigos/lidando-com-desbalanceamento-dados\n", + "https://acervolima.com/ml-manipulacao-de-dados-desequilibrados-com-smote-e-algoritmo-de-quase-perda-em-python/" + ], + "metadata": { + "id": "p6u6B0omgzuq" + } + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": { + "id": "aOkaDiM60gxw" + }, + "outputs": [], + "source": [ + "texto = df_result_merge_text_analise[['Comportamento agressivo?', 'tweet_author', 'tweet_text']]\n", + "texto['Tamanho'] = texto['tweet_text'].apply(len)" + ] + }, + { + "cell_type": "code", + "source": [ + "texto['tweet_text'] = texto['tweet_text'].str.replace(\"rt\", \"\")\n", + "texto['tweet_text'] = texto['tweet_text'].str.replace(\"https\", \"\")\n", + "#texto['tweet_text'] = texto['tweet_text'].str.replace(\"@\", \"\")\n", + "#texto['tweet_text'] = texto['tweet_text'].str.replace(\"#\", \"\")\n", + "texto['tweet_text']" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lZhGsPUOLHQt", + "outputId": "ab7c715f-f6fc-476d-bb06-bbeb2ea76f24" + }, + "execution_count": 281, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 @lucianohangbr já demorou muito!, @lucianohan...\n", + "1 hospício....louca. ://t.co/34bby21hrq, . ://t....\n", + "2 @jairbolsonaro: - rio de janeiro / rj: o @gov...\n", + "3 @rachelsherazade vc chama isso de jornalismo? ...\n", + "4 @brazilfight: janaína paschoal\\n\"jamais um br...\n", + " ... \n", + "829 @claudeluca_: alguém tem notícia de quando vã...\n", + "830 @dindorio te seguindo, patriota !!! sdv ????\\n...\n", + "831 @beta_jesse 👏👏👏👏 por isso #lavajatoorgulhodobr...\n", + "832 @drbots2: ---\\njustiça condena influenciador ...\n", + "833 @camelojubeni @konigmachado @marcos_28_11_66 @...\n", + "Name: tweet_text, Length: 834, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 281 + } + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": { + "id": "KG8nJnA80xhL" + }, + "outputs": [], + "source": [ + "#stopWord = stopwords.words(\"portuguese\")\n", + "stopWords = nltk.corpus.stopwords.words('portuguese')" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "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": 336, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "k0JVUzGr1Iv4", + "outputId": "a8d2997a-3e83-40ca-9ba1-9581cb123c9b" + }, + "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, lucianohangbr, vida, ... 871 \n", + "1 [hospíciolouca, tco34bby21hrq, tcol9zmyju15t, ... 527 \n", + "2 [jairbolsonaro, rio, janeiro, rj, govbr, meio,... 1026 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 252 \n", + "4 [brazilfight, janaína, paschoal, jamais, brasi... 1036 " + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comportamento agressivo?tweet_authortweet_textTamanho
0nãolemathes[lucianohangbr, demorou, lucianohangbr, vida, ...871
1nãoMaurcio98905595[hospíciolouca, tco34bby21hrq, tcol9zmyju15t, ...527
2nãoLunViana[jairbolsonaro, rio, janeiro, rj, govbr, meio,...1026
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...252
4nãoJoseCar41451194[brazilfight, janaína, paschoal, jamais, brasi...1036
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 336 + } + ], + "source": [ + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "id": "3TSR5jYI1XCy", + "outputId": "5c61c9ab-892c-4b9c-f526-8d5d7018e9e7" + }, + "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": 337 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "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": 338, + "metadata": { + "id": "dW1fnIfj1lsj" + }, + "outputs": [], + "source": [ + "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\")" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "1A0s6yxv1wye", + "outputId": "a5664458-474d-4fe6-a11c-ed867f2fa974" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gráfico de frequência de Comportamento agressivo = sim:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = sim:\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='sim'])" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "xxyZtxgw2POm", + "outputId": "a53e5e9d-ae20-4451-e2e8-0aa6cc22a42b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gráfico de frequência de Comportamento agressivo = não:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "print(\"Gráfico de frequência de Comportamento agressivo = não:\")\n", + "grafico_frequencia(texto_preprocessado[texto_preprocessado['Comportamento agressivo?']=='não'])" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": { + "id": "Uh33_5yC2gG_" + }, + "outputs": [], + "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 = (10, 5),\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": 291, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 426 + }, + "id": "lC93YnyQ3PhL", + "outputId": "fd13a0ae-6373-4f8d-d553-03611676490a" + }, + "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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\n" + }, + "metadata": {} + } + ], + "source": [ + "print(\"Nuvem de palavras para agressivo sim:\\n\")\n", + "nuvem_palavras('sim')" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 426 + }, + "id": "Y2swNWf13ngt", + "outputId": "d00d3b13-6bc9-43e0-c0a5-95968c9cb735" + }, + "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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\n" + }, + "metadata": {} + } + ], + "source": [ + "print(\"Nuvem de palavras para agressivo não:\\n\")\n", + "nuvem_palavras('não')" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "kbd6vgVyuI4Y", + "outputId": "5d984566-e18a-4d73-ebc4-907fa5bb7f61" + }, + "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, lucianohangbr, vida, ... 871 \n", + "1 [hospíciolouca, tco34bby21hrq, tcol9zmyju15t, ... 527 \n", + "2 [jairbolsonaro, rio, janeiro, rj, govbr, meio,... 1026 \n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo... 252 \n", + "4 [brazilfight, janaína, paschoal, jamais, brasi... 1036 \n", + ".. ... ... \n", + "829 [claudeluca, alguém, notícia, vão, cassar, man... 1026 \n", + "830 [dindorio, seguindo, patriota, sdv, fechadocom... 680 \n", + "831 [betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas... 669 \n", + "832 [drbots2, justiça, condena, influenciador, ass... 1057 \n", + "833 [camelojubeni, konigmachado, marcos281166, kon... 933 \n", + "\n", + "[834 rows x 4 columns]" + ], + "text/html": [ + "\n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Comportamento agressivo?tweet_authortweet_textTamanho
0nãolemathes[lucianohangbr, demorou, lucianohangbr, vida, ...871
1nãoMaurcio98905595[hospíciolouca, tco34bby21hrq, tcol9zmyju15t, ...527
2nãoLunViana[jairbolsonaro, rio, janeiro, rj, govbr, meio,...1026
3simfelipeleixas[rachelsherazade, vc, chama, jornalismo, vídeo...252
4nãoJoseCar41451194[brazilfight, janaína, paschoal, jamais, brasi...1036
...............
829nãoCesarNi85939384[claudeluca, alguém, notícia, vão, cassar, man...1026
830nãoPauloRo49195361[dindorio, seguindo, patriota, sdv, fechadocom...680
831nãoMarina92011959[betajesse, 👏👏👏👏, lavajatoorgulhodobrasil, tas...669
832nãoMarcos_28_11_66[drbots2, justiça, condena, influenciador, ass...1057
833nãoFATIMAC75843178[camelojubeni, konigmachado, marcos281166, kon...933
\n", + "

834 rows × 4 columns

\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 392 + } + ], + "source": [ + "#Padroniza a saída da classificação do INCT-DD para bot e monta o conjunto Y\n", + "texto_preprocessado" + ] + }, + { + "cell_type": "markdown", + "source": [ + "

Problema 2

" + ], + "metadata": { + "id": "K899au7Ijl3X" + } + }, + { + "cell_type": "markdown", + "source": [ + "Apesar de ter retirado as stopwords, os textos do Twitter conté muitos rt, https, emojis que precisam ser removidos de forma a deixa apenas texto útil. Isso pode ser visualizado nos gráficos de frequência e núvens de palavras que foram gerados.\n", + "\n", + "É necessário pensar numa forma de remover isso do texto, seja por expressão regular ou por meio de código em Python" + ], + "metadata": { + "id": "v61_pjMljnRA" + } + }, + { + "cell_type": "code", + "execution_count": 393, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LtWq7dXtLVFA", + "outputId": "1496fb78-0baa-4317-ee55-5400672ce357" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 [lucianohangbr, demorou, lucianohangbr, vida, ...\n", + "1 [hospíciolouca, tco34bby21hrq, tcol9zmyju15t, ...\n", + "2 [jairbolsonaro, rio, janeiro, rj, govbr, meio,...\n", + "3 [rachelsherazade, vc, chama, jornalismo, vídeo...\n", + "4 [brazilfight, janaína, paschoal, jamais, brasi...\n", + "Name: tweet_text, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 393 + } + ], + "source": [ + "x = texto_preprocessado['tweet_text']\n", + "x.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eXJsyZoo4sVy", + "outputId": "328d0f4b-dc8e-451b-c879-a3078ad82224" + }, + "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": 394 + } + ], + "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": 395, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Dimensões da matrix esparsa: \", x.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yxQ3plNqO46w", + "outputId": "55790437-ecd9-4f27-df23-028065b8ea2d" + }, + "execution_count": 396, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dimensões da matrix esparsa: (834, 102549)\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": 397, + "outputs": [] + }, + { + "cell_type": "code", + "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()" + ], + "metadata": { + "id": "MnmEjK5G0m9I" + }, + "execution_count": 398, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "smt = SMOTE(k_neighbors=5, random_state=42)\n", + "x_train, y_train = smt.fit_resample(x_train, y_train)\n", + "#x_train, y_train = smt.fit_resample(x_train, y_train)\n", + "np.bincount(y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ou3RVFquTyy3", + "outputId": "4f03d049-26b7-4a93-9d84-81366b96898c" + }, + "execution_count": 399, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([524, 524])" + ] + }, + "metadata": {}, + "execution_count": 399 + } + ] + }, + { + "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": 400, + "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": "73e6f7fc-df1c-4768-f3c5-0a14f14ea47d" + }, + "execution_count": 401, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão - Multinomial Naive Bayes:\n", + "\n", + "[[200 12]\n", + " [ 37 2]]\n", + "\n", + "Relatório de Classificação: precision recall f1-score support\n", + "\n", + " 0 0.84 0.94 0.89 212\n", + " 1 0.14 0.05 0.08 39\n", + "\n", + " accuracy 0.80 251\n", + " macro avg 0.49 0.50 0.48 251\n", + "weighted avg 0.73 0.80 0.76 251\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": 402, + "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": "95c7b0f7-fec1-44e2-c7e6-2cc8054f4584" + }, + "execution_count": 403, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de confusão sem normalização\n", + "\n", + "[[200 12]\n", + " [ 37 2]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAAEmCAYAAADfpHMGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3debxd873/8dc7iSkSMoqYGtwYQkkjFWIKMZeGukVQamhoq62qq6r3Vuh1f6pSrpZqkJpjqJmYStVcgjQkqCluEpkjkhCR4fP7Y60TO8cZ9jl77bPXPuf99FiPs/d3rb3W55wjn/Od1voqIjAzs9K0q3QAZmatgZOpmVkGnEzNzDLgZGpmlgEnUzOzDDiZmpllwMnUmkzSOpLul/SxpDtKOM+xkh7NMrZKkbSHpLcqHYdVjjzPtPWSdAxwJrANsAiYAFwYEc+UeN7vAD8CBkfE8pIDzTlJAfSNiHcqHYvll2umrZSkM4HLgP8BegGbAVcCwzI4/VeAf7WFRFoMSR0qHYPlQER4a2UbsD6wGPh2A8esRZJsP0y3y4C10n1DgGnAz4DZwAzgxHTf+cDnwLL0GicDI4GbCs7dBwigQ/r+u8B7JLXj94FjC8qfKfjcYOAl4OP06+CCfU8CvwaeTc/zKNCjnu+tJv6zC+I/DDgY+BcwHzi34PidgeeBBemxfwDWTPc9lX4vn6Tf71EF5/85MBO4saYs/cyW6TUGpO83AuYAQyr9/4a38m2umbZOuwJrA3c3cMwvgV2A/sCOJAnlPwv2b0iSlDcmSZhXSOoaEeeR1HZvi4hOEXFtQ4FIWhe4HDgoIjqTJMwJdRzXDXgwPbY78DvgQUndCw47BjgR2ABYEzirgUtvSPIz2Bj4FXA1cBywE7AH8F+SNk+PXQH8FOhB8rMbCvwAICL2TI/ZMf1+bys4fzeSWvqIwgtHxLskifYmSR2BPwPXR8STDcRrVc7JtHXqDsyNhpvhxwIXRMTsiJhDUuP8TsH+Zen+ZRExjqRWtnUz41kJbC9pnYiYERGT6jjmG8DbEXFjRCyPiLHAm8ChBcf8OSL+FRFLgNtJ/hDUZxlJ//Ay4FaSRPm/EbEovf5kkj8iRMTLEfFCet0pwJ+AvYr4ns6LiKVpPKuJiKuBd4B/AL1J/nhZK+Zk2jrNA3o00pe3EfBBwfsP0rJV56iVjD8FOjU1kIj4hKRpfBowQ9KDkrYpIp6amDYueD+zCfHMi4gV6euaZDerYP+Sms9L2krSA5JmSlpIUvPu0cC5AeZExGeNHHM1sD3w+4hY2sixVuWcTFun54GlJP2E9fmQpIlaY7O0rDk+AToWvN+wcGdEPBIR+5HU0N4kSTKNxVMT0/RmxtQUfySJq29ErAecC6iRzzQ4DUZSJ5J+6GuBkWk3hrViTqatUER8TNJPeIWkwyR1lLSGpIMkXZweNhb4T0k9JfVIj7+pmZecAOwpaTNJ6wO/qNkhqZekYWnf6VKS7oKVdZxjHLCVpGMkdZB0FNAPeKCZMTVFZ2AhsDitNX+/1v5ZwBZNPOf/AuMj4hSSvuCrSo7Scs3JtJWKiFEkc0z/k2QkeSpwOnBPesh/A+OBicBrwCtpWXOu9RhwW3qul1k9AbZL4/iQZIR7L76crIiIecAhJDMI5pGMxB8SEXObE1MTnUUyuLWIpNZ8W639I4HrJS2QdGRjJ5M0DDiQL77PM4EBko7NLGLLHU/aNzPLgGumZmYZcDI1s1ZN0qaS/iZpsqRJkn6SlneT9Jikt9OvXdNySbpc0juSJkoaUMx1nEzNrLVbDvwsIvqR3KjyQ0n9gHOAxyOiL/B4+h7gIKBvuo0gme3RKCdTM2vV0htFXklfLwLeIJm/PAy4Pj3ser6YSjgMuCESLwBdJPVu7Dpt9gEN6rBOaM3OlQ7DGrHDNptWOgQr0j9ffWVuRPTM6nzt1/tKxPIv3VxWp1gyZxJQeBPF6IgYXfs4SX2Ar5HcmdYrImaku2aSPBAIkkQ7teBj09KyGTSg7SbTNTuz1taNznKxCnvi6csqHYIVqXunNWrfwVaSWL6k6H+jn0244rOIGNjQMemNFHcCZ0TEQumL+zIiItJHLTabm/lmllMCtStua+xM0hokifTmiLgrLZ5V03xPv85Oy6cDhU2iTSjiTjwnUzPLJwHt2he3NXSapAp6LfBGRPyuYNd9wAnp6xOAewvKj09H9XcBPi7oDqhXm23mm1kVUGOPSCjKbiRPRHtNUs3jH88FLgJul3QyyUN1avoUxpE8+/YdkgfqnFjMRZxMzSynVFQTvjGRLNNTX1YeWsfxAfywqddxMjWz/MqmZtoinEzNLJ9EJjXTluJkamY5pUYHl/LEydTM8svNfDOzUmUzANVSnEzNLJ+Ea6ZmZqUTtKueFFU9kZpZ29PONVMzs9J4apSZWUbcZ2pmViqP5puZZcOT9s3MSiS5mW9mlgk3883MMuCaqZlZqTwAZWZWupplS6qEk6mZ5VR11UyrJ1Iza3tqRvQb2xo9jcZImi3p9YKy2yRNSLcpNetDSeojaUnBvquKCdU1UzPLr+xqptcBfwBuqCmIiKNWXUYaBXxccPy7EdG/KRdwMjWz/MpoND8inpLUp+5LSCQrk+5TyjXczDezfFK6bEkxG/SQNL5gG9GEK+0BzIqItwvKNpf0qqS/S9qjmJO4ZmpmuaXia6ZzI2JgMy8zHBhb8H4GsFlEzJO0E3CPpO0iYmFDJ3EyNbNcSh60X95J+5I6AN8Cdqopi4ilwNL09cuS3gW2AsY3dC43880sn9SErfn2Bd6MiGmrLiv1lNQ+fb0F0Bd4r7ETOZmaWU4Jqbit0TNJY4Hnga0lTZN0crrraFZv4gPsCUxMp0r9BTgtIuY3dg03880st9q1y6a+FxHD6yn/bh1ldwJ3NvUaTqZmllvl7jPNkpOpmeVT6f2hLcrJ1MxySRTXH5oXTqZmlltOpmZmGchqAKolOJmaWT65z9TMLBtu5puZlcgDUGZmGVE7J1Mzs9LIzXwzs0w4mZqZZcDJ1MysRB6AsrLZpFcXrvn18WzQvTMRMObOZ7li7JN0Xa8jN/7mJL6yUTc++HA+x519LQsWLQFg1Nn/zgG7bcenn33OiPNuZMKb0xq5imXpR98/hUcfGkePnhvw7EsTADjvlz/n4XEPsuaaa9Bn8y35w1XXsH6XLhWONIdUXQNQ1XN7gbF8xUrO+d1dDDjiQvY6/hJOPWpPttliQ846cT+efPEtvjrsAp588S3OOnF/AA7YvR9bbtaT7Yedz+n/PZbLzz26wt9B2zP82BO4/Z4HVisbss++PPvSBJ7+x6ts2bcvl476TYWiy7+snmfaEpxMq8jMuQtX1SwXf7qUN9+fyUY9u3DIkB246f5/AHDT/f/g0L13AOCQvXbglgdeBODF16awfud12LDHepUJvo0avPsedO3abbWyvYfuR4cOSaNw4NcHMWO6Wwv1cTK1stusdzf6b70JL70+hQ26d2bm3GStr5lzF7JB984AbLRBF6bN/GjVZ6bPWsBGG7g5mSe33HgdQ/c/sNJh5Ff5ly3JTNmSqaSQNKrg/VmSRqav15Q0TtLjkq7K6HoHSnpL0juSzsninHm17jprMvaSU/iPS+5k0SeffWl/RAWCsiYbdfH/o337Dnz7qGMqHUpuuWaaWAp8S1KP2jsi4vOIODgihkbEaaVeKF386grgIKAfMFxSv1LPm0cdOrRj7CXf47aHxnPvE/8EYPa8Raua7xv2WI858xcB8OHsBWyyYddVn924Vxc+nL2g5YO2L7nlput59OEH+dOYG3KTDPJGEu3atStqK+JcYyTNlvR6QdlISdMlTUi3gwv2/SKtmL0l6YBi4i1nMl0OjAZ+WnuHpEMl/UPSq5L+KqlXWt5N0j2SJkp6QdIORV5rZ+CdiHgvIj4HbgWGZfWN5MlV5x3LW+/P5PKbnlhV9uDfX+O4QwcBcNyhg3jgyYmryo85ZGcAdv5qHxYuXrKqO8Aq5/HHHuH3l47i5tvupmPHjpUOJ9cyrJleB9TVn3JpRPRPt3HpNfuRLLS3XfqZK2tWK21IuadGXUGyyt/FtcqfAXaJiJB0CnA28DPgfODViDhM0j7ADUB/SXsDl9Zx/k8jYjCwMTC1oHwaMKj2wZJGACMAWKNTSd9YJQzuvwXHHjKI1/41nRduTXoyzvvDfVzy58e46TcnccJhu/J/M+Zz3NljAHj4mUkcsPt2TLrvPD79bBmnjrypkuG3Sd/77nE8+/TfmTdvLttv1YdzfvkrLht1MUuXLuWIbyb/tgd+fRCjLr+ywpHmVEaV9oh4SlKfIg8fBtwaEUuB9yW9Q1Jhe76hD5U1mUbEQkk3AD8GlhTs2gS4TVJvYE3g/bR8d+CI9LNPSOouab2I+BvQP4N4RpPUlmnXcYOq61l8bsJ7rPO10+vcd/Bpv6+z/KcX3V7OkKwRV1/35T9gx51wUgUiqU5N6ALpIWl8wfvR6b/3xpwu6XhgPPCziPiIpHL2QsEx09KyBrXEaP5lwMnAugVlvwf+EBFfBU4F1m7oBJL2LujXKNyeSw+ZDmxa8JFN0jIzq1ZqUjN/bkQMLNiKSaR/BLYkqajNAEY1fHjDyn4HVETMl3Q7SUIdkxavzxfJ7oSCw58GjgV+LWkIyQ9oIdBYzfQloK+kzdPzHg14iNSsignRrox3QEXErFXXkq4Gau6uaFblrKXmmY4CCkf1RwJ3SHoZmFurfCdJE4GLWD3R1isilgOnA48AbwC3R8Sk0sM2s0qSituad271Lnh7OFAz0n8fcLSktdIKWl/gxcbOV7aaaUR0Kng9C+hY8P5e4N46PjMfOKyZ1xsHjGvOZ80sn7KaNiZpLDCEpG91GnAeMERSfyCAKSRdjkTEpLQ1PZlkVtIPI2JFY9fwg07MLJ9KqHXWFhHD6yi+toHjLwQubMo1nEzNLJcEZe0zzZqTqZnllpOpmVmpMmzmtwQnUzPLJeFlS8zMMpCfJ0IVw8nUzHKrinKpk6mZ5ZQ8AGVmVjL3mZqZZaSKcqmTqZnll2umZmalcp+pmVnpkj7TSkdRPCdTM8spzzM1M8tEFeVSJ1Mzyy/XTM3MSiQPQJmZZcM1UzOzDFRRLm2xBfXMzJqsCUs9N3aeMZJmS3q9oOy3kt6UNFHS3ZK6pOV9JC0pWFL+qmJidTI1s3wqcmXSImuv1wEH1ip7DNg+InYA/gX8omDfuxHRP91OK+YCTqZmlktCtGtX3NaYiHgKmF+r7NF0mXiAF4BNSonXydTMcqudVNRGsoTz+IJtRBMvdRLwUMH7zSW9KunvkvYo5gQegDKz3GrCANTciBjYvGvol8By4Oa0aAawWUTMk7QTcI+k7SJiYUPncTI1s1xK+kPLO5wv6bvAIcDQiAiAiFgKLE1fvyzpXWArYHxD53IyNbPcKuecfUkHAmcDe0XEpwXlPYH5EbFC0hZAX+C9xs5XbzKV9Hsg6tsfET9uSuBmZk2V1R1QksYCQ0j6VqcB55GM3q8FPJbWgF9IR+73BC6QtAxYCZwWEfPrPHGBhmqmDVZpzczKSSQj+lmIiOF1FF9bz7F3Anc29Rr1JtOIuL7wvaSOhVVhM7Nyq6Jb8xufGiVpV0mTgTfT9ztKurLskZlZ21bk3U95uX+/mHmmlwEHAPMAIuKfJH0KZmZlleEdUGVX1Gh+REytlf1XlCccM7OEgPZV1M4vJplOlTQYCElrAD8B3ihvWGZm1fUIvmKa+acBPwQ2Bj4E+qfvzczKptgmfl7ybaM104iYCxzbArGYma2mXV4yZRGKGc3fQtL9kuakzwO8N70rwMysrFTklgfFNPNvAW4HegMbAXcAY8sZlJlZzQBUMVseFJNMO0bEjRGxPN1uAtYud2Bm1sZV2TzThu7N75a+fEjSOcCtJPfqHwWMa4HYzKyNy0meLEpDA1AvkyTPmm/n1IJ9weqP+Dczy1xeap3FaOje/M1bMhAzs0KtcdI+krYH+lHQVxoRN5QrKDMzyM9IfTEaTaaSziN5DmA/kr7Sg4BnACdTMysbqZXNMwX+HRgKzIyIE4EdgfXLGpWZGa3sDihgSUSslLRc0nrAbGDTMsdlZlZVA1DF1EzHS+oCXE0ywv8K8HxZozKzNk8UN2G/mEEqSWPSOzhfLyjrJukxSW+nX7um5ZJ0uaR3JE2UNKCYeBtNphHxg4hYEBFXAfsBJ6TNfTOz8sn2QSfXAQfWKjsHeDwi+gKPp+8hGRfqm24jgD8Wc4GGJu3Xm40lDYiIV4q5QF5t23cTbn/gokqHYY3ouJYX0G3LsmrmR8RTkvrUKh5GMrgOcD3wJPDztPyGdOnnFyR1kdQ7ImY0dI2G/k8d1VBswD4NndjMrFTF9EOWoFdBgpwJ9EpfbwxMLThuWlrWvGQaEXuXEKSZWUlEk2qmPSQVrqg8OiJGF/vhiAhJ9S5tXwy3ocwstzoUXzWdGxEDm3j6WTXNd0m9SWYqAUxn9RlLm6RlDSpzLdrMrHmSwaWyPjXqPuCE9PUJwL0F5ceno/q7AB831l8KrpmaWY5ldWu+pLEkg009JE0DzgMuAm6XdDLwAXBkevg44GDgHeBToKjZS8XcTiqSZUu2iIgLJG0GbBgRLzbt2zEza5qs5uxHxPB6dg2t49igGevcFdPMvxLYFagJZhFwRVMvZGbWFCK5N7+YLQ+KaeYPiogBkl4FiIiPJK1Z5rjMzGifjzxZlGKS6TJJ7UnmliKpJ7CyrFGZWZunHNU6i1FMM/9y4G5gA0kXkjx+73/KGpWZGa3sqVERcbOkl0k6agUcFhFvlD0yM2vzquhB+0WN5m9GMj3g/sKyiPi/cgZmZm1bzQBUtSimz/RBvlhYb21gc+AtYLsyxmVmbZ2gfRXdVlRMM/+rhe/Tp0n9oGwRmZmlVEWrQDX5DqiIeEXSoHIEY2ZWI2nmVzqK4hXTZ3pmwdt2wADgw7JFZGaWalXJFOhc8Ho5SR/qneUJx8zsC9W0BlSDyTSdrN85Is5qoXjMzIBk/mirGICS1CEilkvarSUDMjOr0VqmRr1I0j86QdJ9wB3AJzU7I+KuMsdmZm1YqxuAIplbOo9kzaea+aYBOJmaWVlVUcW0wWS6QTqS/zpfJNEaJa2VYmbWGCHaV1E2bSiZtgc6QZ2zZp1Mzay81Hqa+TMi4oIWi8TMrJbWMgBVPd+FmbU6yVLPGZxH2hq4raBoC+BXQBfge8CctPzciBjX3Os0lEy/tDaKmVlLyqJmGhFvAf1h1dz56STPaD4RuDQiLin5IjSQTCNifhYXMDNrDlGWZUuGAu9GxAdZ311VRfcXmFmbouR20mI2kiWcxxdsI+o569HA2IL3p0uaKGmMpK6lhOtkama5pSI3YG5EDCzYRn/pXMlCoN8kuQEJ4I/AliRdADOAUaXE2uRH8JmZtYQyPGn/IOCViJgFUPMVQNLVwAOlnNw1UzPLrSbUTIsxnIImvqTeBfsOJ7lBqdlcMzWznBLtMpq1L2ldYD/g1ILiiyX1J7kJaUqtfU3mZGpmuSSyazpHxCdA91pl38no9ICTqZnlWKt5OLSZWSVVTyp1MjWzvJJrpmZmJUvugHIyNTMrWfWkUidTM8uxKqqYOpmaWT4lU6OqJ5s6mZpZbrlmamZWMrWaJ+2bmVWMm/lmZlmQm/lmZplwMrWyW/rZZ5xwxIF8/vlSVqxYzn4HH8bpZ/2S47+1P58sXgzA/Hlz+Gr/nbj82lsrHK0BTJ06lVNOPJ7Zs2chiZNOHsHpP/5JpcPKNbmZb+W25lprMeb2B+i4bieWLVvG8Yfvzx5778cNdz266pgzvncsex/wjQpGaYU6dOjARReP4msDBrBo0SIGD9qJofvux7b9+lU6tFyqtjug/HDoKiWJjut2AmD58mUsX75stfuYFy9ayIvPPcXQAw6pVIhWS+/evfnagAEAdO7cmW222ZYPP5xe4ajyTSpuywMn0yq2YsUKjth/MHvuuAW77rE3Owz4+qp9jz/yAIN224tOnderYIRWnw+mTGHChFf5+s6DKh1KrqnI//Kg7MlU0i8lTUpXAJwgaZCkayRl2raRdKGkqZIWZ3nePGvfvj13Pvocj7/0Jq9NeJm335y8at9D9/yFg4d9u4LRWX0WL17M8COP4LejLmO99fzHrj7JGlDFbXlQ1mQqaVfgEGBAROwA7AtMjYhTImJyw59usvuBnTM+Z1VYb/0u7Dx4T5558jEAPpo/l9cmjGfPoQdUODKrbdmyZQw/8giOGn4shx3+rUqHk29KJu0Xs+VBuWumvUmWYF0KEBFzI+JDSU9KGgggabGk36a1179K2jnd/56kbxZ7oYh4ISJmlOn7yJ358+aw8OMFAHy2ZAnPP/0Em//bVgA8+uC97LXvgay19tqVDNFqiQhO+97JbL3Ntvzkp2dWOpyqkNWCepKmSHotbR2PT8u6SXpM0tvp166lxFruZPoosKmkf0m6UtJedRyzLvBERGwHLAL+m2Thq8OBCwAkbZ3+EOrauhQbjKQRksZLGv/RvLmlf3cVNGfWLE468hscvu8uHH3IXuy6xz4M2fcgAB661038PHru2We55eYb+fvfnmDQTv0ZtFN/Hn5oXKXDyq2apZ4zrJnuHRH9I2Jg+v4c4PGI6As8nr5vtrJOjYqIxZJ2AvYA9gZuk1Q74M+Bh9PXrwFLI2KZpNeAPul53gL6ZxDPaGA0wHY7DohSz1dJW/fbnr888myd+677y0MtHI0VY7fdd2fJsqr+367FlbkBPwwYkr6+HngS+HlzT1b2eaYRsYIkyCfTBHlCrUOWRUTN/2ErgZougZWSOkBSMwVuq+cSQyJiQeaBm1nlFZ9Ne9Q031Oj08pTjQAelRTAn9J9vQq6BmcCvUoJtazJNE2CKyPi7bSoP/ABsH1TzpNVzdTMqksTmvBzC5rvddk9IqZL2gB4TNKbhTsjItJE22zl7jPtBFwvabKkiUA/YGQ5LiTpYknTgI6Spkkqy3XMrOVkNQAVEdPTr7OBu0lm/syS1Bsg/Tq7lFjL3Wf6MjC4jl1DCo7pVPB6ZK3Pd6JIEXE2cHaTgzSz/Mqg01TSukC7iFiUvt6fZHD7PpJux4vSr/eWch3fm29muZTUOjMZguoF3J3ebt0BuCUiHpb0EnC7pJNJuh+PLOUiTqZmlk8Z3XcfEe8BO9ZRPg8YWvoVEk6mZpZbObm5qShOpmaWU/l5iEkxnEzNLLdcMzUzK1Gx057ywsnUzPKrirKpk6mZ5VZeHq9XDCdTM8ut6kmlTqZmlldV1mnqZGpmueWpUWZmJRKeGmVmlgknUzOzDLiZb2aWAddMzcwyUEW51MnUzHKsirKpk6mZ5ZLkO6DMzDJRPanUydTM8qyKsmm5Vyc1M2smFf1fg2eRNpX0t3SV5EmSfpKWj5Q0XdKEdDu4lGhdMzWzXBLQLpua6XLgZxHxiqTOwMuSHkv3XRoRl2RxESdTM8uvbBbUmwHMSF8vkvQGsHHpZ16dm/lmlltNaOb3kDS+YBtR5/mkPsDXgH+kRadLmihpjKSupcTqZGpmuSUVtwFzI2JgwTb6y+dSJ+BO4IyIWAj8EdgS6E9Scx1VSqxOpmaWWypya/Q80hokifTmiLgLICJmRcSKiFgJXA3sXEqsTqZmlk8CSUVtDZ4mOeBa4I2I+F1Bee+Cww4HXi8lXA9AmVkuZfg8092A7wCvSZqQlp0LDJfUHwhgCnBqKRdxMjWz3Moil0bEM/WcalwGp1/FydTMcquKbs13MjWz/PLDoc3MMuCaqZlZiQrmkFYFJ1Mzyy03883MslA9udTJ1Mzyq4pyqZOpmeWVvGyJmVmpMrwDqkX43nwzswy4ZmpmuVVNNVMnUzPLLU+NMjMrkZTZGlAtwsnUzPLLydTMrHRu5puZZcADUGZmGaiiXOpkamb51dj6TnniZGpmuVRtd0ApIiodQ0VImgN8UOk4MtYDmFvpIKworfF39ZWI6JnVySQ9TPJzKsbciDgwq2s3R5tNpq2RpPERMbDScVjj/LtqfXxvvplZBpxMzcwy4GTauoyudABWNP+uWhn3mZqZZcA1UzOzDDiZmpllwMnUzCwDTqZtgKQdJH1DUndJa1c6HqufJN+VWKU8ANXKSRoG/C/wErAGMB64JSLeq2hgthpJg4A3I+JjSe0jYkWlY7Kmcc20FVPylIj9gRER8W3gCqA9cIakzSsanK0i6QDgeWCSpO4RscI11OrjZNr6dQGGAETEY8B9wDzg25LWqGBcBkjqCBwIHAz8GXgpTajLnVCri5NpKxZJH875wLaSjkzLXiVp8u8FrFPB8AyIiE9JumGei4j/Ivlj95KknhGxvLLRWVM4mbZSkmp+t1OAu4ADJB0FEBHjgJXAgMpEZ4UiYgqwKH19BklCfRFA0h6SDq1cdFYsNyNaoZoBDEmbAD2Be4EVwHckDQTeAfoBb1cwzDav4PfULiJWSuoQEcsj4gxJH0laCUwH9q50rNY410xbmVqJ9AGgD7A4Im4Bfgx0BLYEvhUR0ysXadtW8Hv6CnCtpLXSftKafuxXSfq2D4iIdyoXqRXLNdNWpFYivYOkv3Qi8Jykw9LpUD+saJBW+/d0C8ksi+6SPo+IuZK6A0OBfSJickWDtaK5ZtpKSFL6D3RT4Dbgt8AEYCzwm4iYWdEADajzD94okmlRzwHbAkTEPODsiHitcpFaUzmZthIREZK6kPwDvYykmfgX4NcRcbeqaWWyVqygaX8HcDHJ7+kO4PSIeLpm4DAillYwTGsG3wFVpdKaaBS+J5mQvx2wkKS/9OcR8UCFQjTq/T2NIpme9gJwO3BBRNxfoRAtI06mVU7S94G+QC/gnIiYKuknwPsRcV9lo7MakrYGVkbE25LaAxsCTwFnOJG2Dk6mVUzSd4DTgbOAw0luHR0WEe9WNDBbrUYq6T+AE4GZwKSI+JGkbsA2EfFcJeO07LjPtIpIWqtW0bbAtRHxdEScSdJkvCu9RdEqpFYiXRtYD9gTOAzYV9KfImJ+RDznW0ZbDyfTKiFpH2BkwZ1NAAuAVQ8siYgLSEbwayddayHpBPyaRPoD4DpgJ6BnRCwEvg7sLukWAN8y2nr4r2IVkDQE+JXL3EYAAAXPSURBVDVwNMk/xCBJpDcCD0qaCYwj+Ye6I/4jWTERsRJA0l7AMJJbQ/cGDpG0MiLekrQL8ISk3sDMcF9bq+A+0yogaQAwB9gV+BlJc/5Q4FfAu8AlwOfAV4AfRsSkCoXaZknqC8yNiI/Se+mvB46MiL+mj9jbH5gFPBgRk2qP8lv1cw0mxyRtJqlzRLxCMvr7PWA3oAcgktrq9hExnGSA43An0paXDib9iOT5B6Sj8+8C56XvHwEeIrmNd38/+rB1cs00pyT1As4FppLcbrguySPz9iJJqgeQ3C56JPBfEXFThUJt0yQdQtInegPQGxhOMt1phaQXgRkRMSw9dm9gckTMqljAVjaumebXHJKJ3RsBJwOfRMRUkhrq5RHxWXrMTcDTFYuyDUsT6YXAPyPifZLHHfYFzk+fALUz0FXSkwAR8Tcn0tbLyTRnJPWVtHU6kHEz8DdgG2BEOs1mCfALSRcA3weuiYgPKhdx2yRpQ5L+61Mi4h5JHSPiQ2AkSd/2mZLWjIg9gc/TZyZYK+bR/BxJnxb0FjBX0vkkfXCjgfVJpkCdGBFXSJpPcsfTN51IK2YpsAz4LP0jd3Y6gj8fCJIBwp6Sfh4R+1cwTmshTqY5EhHzJO0L/JWk1bAjyROgFpOM1veXtAIY4/mJFbcAeIRkJsV2JL+zm4E3SO5G+wDYhWSwcHaFYrQW5AGoHJK0H3A5STLtBexDMsd0Z5JbEgdHxMeVi9AAJHUCvgpsCtxb86QnSdcDt0bEQ5WMz1qWk2lOSfoGcCmwS0TMl9SVZN37jumaQZZDkr4NnAMc5Sfkty1u5udURDyYrgH0gqRd0wcGW06ldzMdRTJtzYm0DXIyzbGIeEjSmsBfJe1Uc6ui5dICkgUKhzmRtk1u5lcBSZ0iYnGl4zCz+jmZmpllwJP2zcwy4GRqZpYBJ1Mzsww4mZqZZcDJ1FYjaYWkCZJel3RHKetJSbpO0r+nr6+R1K+BY4dIGtyMa0yR1KPY8lrHNGmGhKSRks5qaozWNjiZWm1LIqJ/RGxP8jyA0wp3NncBuIg4JSImN3DIEKDJydQsL5xMrSFPA/+W1hqflnQfMFlSe0m/lfSSpImSToVkVU5Jf5D0lqS/AhvUnEjSk5IGpq8PlPSKpH9KelxSH5Kk/dO0VryHpJ6S7kyv8ZKk3dLPdpf0qKRJkq4hWXGgQZLukfRy+pkRtfZdmpY/LqlnWralpIfTzzwtaZssfpjWuvkOKKtTWgM9CHg4LRpAskTK+2lC+jgivq5k+elnJT0KfA3YGuhH8oCWycCYWuftCVwN7Jmeq1v67IGrgMURcUl63C3ApRHxjKTNSJ7QtC3JUiDPRMQF6fMLTi7i2zkpvcY6wEuS7kxvz10XGB8RP5X0q/Tcp5M89vC0iHhb0iDgSpKHzZjVy8nUaltH0oT09dPAtSTN7xfTp8lDsjjcDjX9oSTPW+1Lsjb82IhYAXwo6Yk6zr8L8FTNuSJifj1x7Av0k1ZVPNdLn9K0J/Ct9LMPSvqoiO/px5IOT19vmsY6D1hJ8ohDSFYsuCu9xmDgjoJre+lsa5STqdW2JCL6FxakSeWTwiLgR+lCcYXHHZxhHO1Inpj1WR2xFE3JMtn7ArtGxKfpEiJr13N4pNddUPtnYNYY95laczwCfF/pKpuStpK0LvAUcFTap9qbZL342l4A9pS0efrZbmn5IqBzwXGPkqz4SXpcTXJ7CjgmLTsI6NpIrOsDH6WJdBuSmnGNdkBN7foYku6DhcD76aP0avqBd2zkGmZOptYs15D0h74i6XXgTyStnLtJnpw0mWS1zudrfzAi5gAjSJrU/+SLZvb9wOE1A1DAj4GB6QDXZL6YVXA+STKeRNLc/79GYn0Y6CDpDeAikmRe4xNg5/R72Ae4IC0/Fjg5jW8SMKyIn4m1cX7QiZlZBlwzNTPLgJOpmVkGnEzNzDLgZGpmlgEnUzOzDDiZmpllwMnUzCwD/x8OBGzh/GOxCQAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "tfidf_transformer = TfidfTransformer()\n", + "x_tfidf = tfidf_transformer.fit_transform(x)\n", + "\n", + "x_tfidf_train, x_tfidf_test, y_tfidf_train, y_tfidf_test = train_test_split(x_tfidf,y, test_size=0.3, random_state=42)\n", + "\n", + "mnb_tfidf = MultinomialNB()\n", + "\n", + "mnb_tfidf.fit(x_tfidf_train,y_tfidf_train)\n", + "pred_mnb_tfidf = mnb_tfidf.predict(x_tfidf_test)" + ], + "metadata": { + "id": "JH7C8CL3t6KD" + }, + "execution_count": 404, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Matriz de Confusão - Multinomial Naive Bayes com TF-IDF:\")\n", + "print(confusion_matrix(y_tfidf_test,pred_mnb_tfidf))\n", + "print(\"Relatório de Classificação:\",classification_report(y_tfidf_test,pred_mnb_tfidf))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dr_wCsEXuPSA", + "outputId": "c3f8ea4c-fb7a-4ae2-adf3-5c6d4cf86506" + }, + "execution_count": 405, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão - Multinomial Naive Bayes com TF-IDF:\n", + "[[212 0]\n", + " [ 39 0]]\n", + "Relatório de Classificação: precision recall f1-score support\n", + "\n", + " 0 0.84 1.00 0.92 212\n", + " 1 0.00 0.00 0.00 39\n", + "\n", + " accuracy 0.84 251\n", + " macro avg 0.42 0.50 0.46 251\n", + "weighted avg 0.71 0.84 0.77 251\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Calculando a confusion matrix\n", + "matriz_confusao1 = confusion_matrix(y_tfidf_test, pred_mnb_tfidf, 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_confusao1, classes=['Liked=0','Liked=1'],normalize= False, title='Confusion matrix')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380 + }, + "id": "QspPyxv8ubq8", + "outputId": "bc86dd58-4cf3-4d58-9c01-e761ae5e70f3" + }, + "execution_count": 406, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de confusão sem normalização\n", + "\n", + "[[212 0]\n", + " [ 39 0]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "rmfr = RandomForestClassifier()\n", + "rmfr.fit(x_train,y_train)\n", + "predrmfr = rmfr.predict(x_test)" + ], + "metadata": { + "id": "FwdkqujSuu5G" + }, + "execution_count": 407, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(\"Matriz de Confusão - Random Forest:\")\n", + "print(confusion_matrix(y_test,predrmfr))\n", + "print(\"Relatório de Classificação:\",classification_report(y_test,predrmfr))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EqcsEVX4uz6-", + "outputId": "59998860-3bf5-4f63-842c-ab0b02b2644b" + }, + "execution_count": 408, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de Confusão - Random Forest:\n", + "[[203 9]\n", + " [ 38 1]]\n", + "Relatório de Classificação: precision recall f1-score support\n", + "\n", + " 0 0.84 0.96 0.90 212\n", + " 1 0.10 0.03 0.04 39\n", + "\n", + " accuracy 0.81 251\n", + " macro avg 0.47 0.49 0.47 251\n", + "weighted avg 0.73 0.81 0.76 251\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Calculando a confusion matrix\n", + "matriz_confusao2 = confusion_matrix(y_test, predrmfr, 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_confusao2, classes=['Liked=0','Liked=1'],normalize= False, title='Confusion matrix')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380 + }, + "id": "RokTegdCu50m", + "outputId": "8a54d6e8-63a5-4ea1-edcf-66eeb6da0432" + }, + "execution_count": 409, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Matriz de confusão sem normalização\n", + "\n", + "[[203 9]\n", + " [ 38 1]]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "

Análise do Resultado

" + ], + "metadata": { + "id": "1SS9DzQIkdIC" + } + }, + { + "cell_type": "markdown", + "source": [ + "Pela matriz de confusão é possível observar que o modelo foi muito assertivo em classificar a classe \"agressivo = não\", justamente por ela ser a classe majoritária acertando 198 e errando apenas 14, e foi muito ruim em classificar a classe \"agressivo = sim\", acertando apenas 1.\n", + "\n" + ], + "metadata": { + "id": "pgSLzfrnkjGi" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T5zR-UdTtiFE" + }, + "source": [ + "

Classificação do tipo de bot

" + ] + }, + { + "cell_type": "markdown", + "source": [ + "

Objetivo

" + ], + "metadata": { + "id": "4BnkdlhPmZYW" + } + }, + { + "cell_type": "markdown", + "source": [ + "Criar um novo atributo que indique a classificação do tipo de bot tomando com esse rótulo já existente no dataframe df_handles.\n", + "\n", + "Para esse classificador é necessário analisar os demais atributos dos outros dataframes para um levantamento dos que sejam mais úteis para ajudar na modelagem desse classificador." + ], + "metadata": { + "id": "IcFykRK_mZ8C" + } + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gSt9oJ-Htnqj", + "outputId": "ced0bcc8-aae9-440e-fc70-b702324aa947" + }, + "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": 410 + } + ], + "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" + ] + }, + { + "cell_type": "markdown", + "source": [ + "

Classifiacação de emoji

" + ], + "metadata": { + "id": "9KY_o1_c-VD4" + } + }, + { + "cell_type": "markdown", + "source": [ + "

Objetivo

" + ], + "metadata": { + "id": "TbBWK7UO-cFS" + } + }, + { + "cell_type": "markdown", + "source": [ + "Classificar os emoji de acordo com o signifcado dos mesmos\n", + "\n", + "\n", + "O link abaixo lista os principais emoji\n", + "https://gist.github.com/rxaviers/7360908" + ], + "metadata": { + "id": "k2F1-OXc-iUO" + } + }, + { + "cell_type": "markdown", + "source": [ + "A figura abaixo mostra a quantidade dos emojis mais usados agrupados por categorias (Amor, Felicidade, etc)" + ], + "metadata": { + "id": "daZHh3mpCYuJ" + } + }, + { + "cell_type": "markdown", + "source": [ + "![IMG-20220904-WA0000.jpg](data:image/jpeg;base64,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)" + ], + "metadata": { + "id": "PQoBN5xJCMJu" + } + }, + { + "cell_type": "markdown", + "source": [ + "

Quantidade de vezes que um perfil responde tweets em um espaço de tempo

" + ], + "metadata": { + "id": "kHUqaJsnSzHn" + } + }, + { + "cell_type": "markdown", + "source": [ + "

Objetivo

" + ], + "metadata": { + "id": "ycr_bC3ES8CQ" + } + }, + { + "cell_type": "markdown", + "source": [ + "Criação de atributo quantitativo para indicar quantidade de vezes que um perfil responde tweets em um espaço de tempo. Foi verificado que a API do Twitter já fornece o dado \"tweets e respostas\" " + ], + "metadata": { + "id": "NdIG4DAqS8iw" + } + } + ], + "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" + } }, - { - "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