Cryptocurrency Price Prediction Using Twitter and News Articles Analysis

Abstract

Due to the redefining of money and its price volatility, cryptocurrencies have become one of the most prominent phenomena in recent years. This research investigates how well public opinion on Twitter and news stories may be used to estimate cryptocurrency returns. Three models are designed and compared: LSTM based, LSTM-and-GRU-based, and LSTM and CNN-based models. Firstly, numerals and historical datasets of Bitcoins are used for all three models, which are further extended to Twitter and news datasets. An error score of 1015.17, 1106.71, and 3010.63 is obtained. Then, the proposed models are applied to the combined dataset of Twitter and news from Ethereum, and an error score of 47.85, 34.01, and 58.27 is obtained. Finally, the same methodology is applied to the combined dataset of Litecoin and obtained an error score of 9.45, 8.81, and 15.94. It is observed that LSTM with GRU generates the best results for all the datasets.