Cryptocurrency analysis: Price prediction of cryptocurrency using user sentiments and quantitative data

This research introduces an innovative approach to forecasting cryptocurrency prices by combining user-generated content (UGC) and sentiment analysis with quantitative data. The primary goal is to overcome limitations in existing methods for market forecasting, where accurate forecasting is crucial for informed decision-making and risk mitigation. The paper suggests a robust prediction methodology by integrating sentiment analysis and quantitative data. The study reviews prior research on sentiment analysis and quantitative analysis of cryptocurrency and stock price prediction. It explores the integration of machine learning and deep learning techniques, an area not extensively explored before. The methodology employs Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Bidirectional LSTM and Gated Recurrent Unit (GRU) models to capture temporal dependencies. Prediction accuracy is assessed using metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and a confusion matrix. Results show that GRU models excel in prediction, while RNN models outperform in predicting price movements; with an emphasis on the significance of a suitable data preprocessing pipeline towards improving model performance. In summary, this study demonstrates the effectiveness of integrating sentiment analysis and quantitative data for cryptocurrency price forecasting using UGC data.