Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis

Abstract

The sentiment in Twitter about Bitcoin have direct or indirect influence on overall market value of the Bitcoin. This research is concerned with predicting the volatile price of Bitcoin by analyzing the sentiment in Twitter and to find the relation between them. The tweets of Bitcoin collected from different news account sources are classified to positive or negative sentiments. The obtained percentage of positive and negative tweets are feed to RNN model along with historical price to predict the new price for next time frame. The accuracy for sentiment classification of tweets in two class positive and negative is found to be 81.39 % and the overall price prediction accuracy using RNN is found to be 77.62%.