Cryptocurrency Price Prediction using Social Media Sentiment Analysis

Abstract

In a paper that was anonymously published and signed by the pseudonym Satoshi Nakamoto, Bitcoin was introduced to the world. Due to its enormous success, a great number of cryptocurrencies were created in the upcoming years. This exponential growth relies mostly on the extreme volatility of the market, which led many people to become interested and get involved, primarily for profit. Cryptocurrency enthusiasts tend to share and learn news and opinions on social media platforms, one of the most popular being Twitter. In this paper, we study the extent to which Twitter sentiment analysis can be used to predict price fluctuations for cryptocurrencies. Initially, we gathered tweets and price data of seven of the most popular cryptocurrencies, which were processed to perform sentiment analysis using Valence Aware Dictionary for Sentiment Reasoning (VADER). The time-series stationarity was determined with Augmented Dicky Fuller (ADF) Kwiatkowski Phillips Schmidt Shin (KPSS) tests and then Granger Causality testing took place. While price fluctuations seem to cause sentiment for Bitcoin, Cardano, XRP and Doge, predictability was found for Ethereum and Polkadot, based on a bullishness ratio. Finally, predictability of price returns is examined with Vector Autoregression (VAR) and highly accurate forecasts for two of the seven cryptocurrencies were achieved. More specifically, price forecasts of Ethereum’s and Polkadot’s prices reached 99.67% and 99.17% accuracy, respectively.