Cryptocurrency Price Prediction Model Based on Sentiment Analysis and Social Influence

Cryptocurrency as an alternative method of payment that acts both as a type of currency and as a virtual accounting system has always been of interest to investors. Since the public sentiment of a society about cryptocurrencies can affect the cryptocurrencies’ prices, a machine learning model based on sentiment analysis has been proposed to forecast the future prices of cryptocurrencies such as Bitcoin, Ethereum, EOS, Cardano, and Ripple using machine learning models that are suitable for time series data analysis to reduce the risk of investing in this market. It was shown that by applying weights to the sentiment scores of tweets according to the influence factor of the individuals, the accuracy of the prediction will increase and a significant difference between the accuracy scores was observed using the LSTM model according to the MAPE indicator $(P=0.045)$ . Also, a hybrid model is proposed based on the combination of features extracted from the texts by one of the dictionary-based text analysis models and the feature of weighted sentiment scores. It was shown that our proposed hybrid model outperformed the other models in predicting the prices of Ethereum, EOS, and Cardano according to the MSE indicator. Also, our proposed model based on weighted sentiment scores according to the influence factor of the Twitterers outperformed the other models in the prediction of the future prices of Bitcoin and Ripple, which indicates that the increase in the number of features will not always lead to an increase in the accuracy of our prediction models.