Cryptomatter-project

Data Overview

• Bitcoin tweets from 01.01.2017 to 26.11.2022      (Username, Handle, Date, Text, Reply_cnt,Retweet_cnt, Like_cnt)
• Bitcoin data from Binance      (first_currency, second_currency, UNIX timestamp, open, high, low, close,    volume)
• BTC trading data      (Date, MarketCap, TradeVolume, ClosePrice)

Parsing tweets from Twitter

• Selenium web parses was used to get all Bitcoin related Tweets.
• As a parameters for proper data capturing we used:
• Minimum # of tweets
• Data range corresponding our BTC data
• Optionally (Retweets, Shares, etc)

Data Understanding: Sentiments for Tweets

• Our aim is to know what was the mood of people about  Bitcoin on a specific day.
• We derived sentiment intensity of each tweet and grouped it by a Date

Our dataset:

• 4 exogenous variables (positive, neutral, negative, block reward)
• 1 time series variable – closing price
• Since the value tn−1 is required to predict tn, and tn−1 is unknown, a recursive process is applied in which, each new prediction, is based on the previous one. This process is known as recursive forecasting or recursive multi-step forecasting