这篇论文用链上交易数据和推特情绪来给比特币情绪分类，XGBoost模型F1到了0.84，还用了SHAP解释特征重要性，对做加密货币分析的人有参考价值。
该研究提出一种结合链上数据、金融指标和推特情绪来分类比特币市场情绪的新方法，而非预测价格。使用XGBoost模型进行情绪分类，平均F1-score达到约0.84。通过SHAP方法量化不同链上特征对模型预测的贡献，增强了可解释性。结果显示该数据组合能提供有意义的预测信号，支持数据驱动的加密货币分析。
Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach to analyze Bitcoin market sentiment by combining on-chain and financial data with social media posts. Unlike models that aim to predict prices, this work focuses on explaining market sentiment using blockchain transactions, historical price data of Bitcoin, and daily Twitter sentiment classifications. The method merges sentiment trends with on-chain and financial metrics, normalized into a dataset for detailed market analysis. Multiple machine learning models were tested using cross-validation, with Gradient Boosting (XGBoost) emerging as the most reliable model for classifying sentiment, achieving an average F1-score of about 0.84. SHAP (SHapley Additive exPlanations), a game theory-based method for model interpretability, was used to quantify the contribution of on-chain features to the model's predictions, improving transparency. The results indicate that this data combination yields meaningful predictive signals and insights, supporting data-driven cryptocurrency analysis and future improvements with deep learning.