用链上数据+推特情绪分析比特币市场情绪，准确率超84%
Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
- 融合链上交易、价格数据与推特情绪，构建情感分类模型
- XGBoost模型在交叉验证中达0.84平均F1分数
- 通过SHAP解释特征贡献，提升模型可解释性
比特币作为去中心化数字资产和投资工具的广泛应用，激发了对其市场行为的深入研究兴趣。本文提出一种新方法，结合链上数据、金融数据与社交媒体内容分析比特币市场情绪。不同于旨在预测价格的模型，本研究聚焦于利用区块链交易、比特币历史价格及每日推特情绪分类来解释市场情绪。该方法将情绪趋势与链上及金融指标融合，并归一化为数据集以支持详细市场分析。采用交叉验证测试多种机器学习模型，其中梯度提升（XGBoost）表现最佳，平均F1-score达到约0.84。同时使用基于博弈论的SHAP（SHapley Additive exPlanations）方法量化链上特征对预测的贡献，增强模型透明度。结果表明，该数据组合能提供有意义的预测信号与洞察，支持数据驱动的加密货币分析，并为未来引入深度学习提供基础。
原文摘要 · Abstract (English)
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.
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