Abstract
In recent years, many investors have used cryptocurrencies, prompting specialists to find out the factors that affect cryptocurrencies' prices. Therefore, one of the most popular methods that have been used to predict cryptocurrency prices is sentiment analysis. It is a widespread technique utilized by many researchers on social media platforms, particularly on Twitter. Thus, to determine the relationship between investors' sentiment and the volatility of cryptocurrency prices, this study forecasts the cryptocurrency prices using the Long-Term-Short-Memory (LSTM) deep learning algorithm. In addition, Twitter users' sentiments using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning approaches are analyzed. As a result, in the classification of the bitcoin (BTC) and Ethereum (ETH) datasets of investors' sentiments into (Positive, Negative, and Neutral), the SVM algorithm outperformed the NB algorithm with an accuracy of 93.95% and 95.59%, respectively. Furthermore, the forecasting regression model achieves an error rate of 0.2545 for MAE, 0.2528 for MSE, and 0.5028 for RMSE.