Bitcoin Price Prediction Based on Sentiment Analysis

Abstract

Bitcoin is a decentralized digital currency that has received a lot of interest in recent years because of its unique qualities and possibilities as an alternative investment. Trading in Bitcoin might be difficult owing to the extreme volatility of its price, which is impacted by a variety of variables such as market sentiment and regulatory changes. To solve this issue, researchers and traders have been investigating the use of social media data, namely Twitter data, to forecast Bitcoin price changes using sentiment analysis. The work focuses on leveraging real-time Twitter data and bitcoin prices from the Twitter and Coin Market API respectively to predict short-term Bitcoin price movements. Real-time tweets are collected, sentiment is extracted using sentiment analysis methods, and then combined with relevant pricing data. A predictive framework is developed to forecast the next hour's Bitcoin price by employing univariate and multivariate time series forecasting and generating deep learning models such as LSTM, BIGRU, RNN and LSTM+GRU. Multivariate time series forecasting model based on BIGRU has performed well among the deep learning models used, attaining a 130.529 RMSE score. The primary aim of this study is to provide valuable insights to traders, as short-term market sentiment plays a crucial role in their trading strategies.