Combining Social Media Sentiment and High-Dimensional Indicators for Bitcoin Price Range Prediction with Cart

In this study, I propose a method for forecasting the next-day Bitcoin price range by integrating natural language processing (NLP) techniques with a Long Short-Term Memory (LSTM) network. The model leverages high-dimensional technical indicators combined with sentiment features extracted from Twitter data through advanced NLP methods to capture the nuanced market sentiment. By incorporating sequential analysis of both numerical market indicators and textual sentiment data, the LSTM model effectively learns temporal dependencies and complex patterns, enhancing the prediction capability for Bitcoin price movements. The experiments utilize Bitcoin market data spanning six years alongside millions of relevant Twitter posts. The approach demonstrates the value of combining deep learning architectures with sentiment analysis to improve forecasting robustness and interpretability in volatile cryptocurrency markets. Sensitivity analysis is applied to optimize the influence of sentiment features, highlighting the importance of sentiment-driven insights in financial prediction models and offering a novel perspective for more accurate and dynamic cryptocurrency market forecasting.