Sentiment-Driven Bitcoin Price Range Forecasting: Enhancing CART Decision Trees With High-Dimensional Indicators and Twitter Dynamics

In this study, I propose a method for forecasting the next-day Bitcoin price range using a CART decision tree model, which integrates 124 high-dimensional technical indicators with Twitter-roBERTa sentiment analysis as the 125th feature to enhance prediction accuracy. The experiments utilize Bitcoin market data from the past six years (2019 to 2024) and approximately 58 million Twitter posts. The results demonstrate that the enhanced model, incorporating sentiment analysis, improves the average accuracy from 0.56 in the baseline model—trained solely on 124 technical indicators—to 0.62, with win rates increasing significantly by up to 45%. Sensitivity analysis further optimizes the sentiment feature weight, confirming the model’s robustness, and provides an innovative perspective for cryptocurrency market prediction, with future applications extensible through multi-source data fusion.