发表机构
Computing Sciences Center - C3（计算科学中心 - C3）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通过结合链上、金融数据与社交媒体帖子分析比特币市场情绪，用梯度提升等模型分类，SHAP量化特征贡献，此数据组合产生有意义信号与见解，支持加密货币分析及深度学习改进。
AI 中文摘要
比特币作为去中心化数字资产和投资工具的使用日益增加，引发了人们对理解其市场行为的浓厚兴趣。本研究提出了一种新方法，通过将链上和金融数据与社交媒体帖子相结合来分析比特币市场情绪。与旨在预测价格的模型不同，这项工作专注于使用区块链交易、比特币历史价格数据和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和金融指标合并，归一化为一个数据集用于详细的市场分析。使用交叉验证测试了多个机器学习模型，梯度提升（XGBoost）成为最可靠的情绪分类模型，平均F1分数约为0.84。基于博弈论的模型可解释性方法SHAP用于量化链上特征对模型预测的贡献，提高了透明度。结果表明，这种数据组合产生了有意义的预测信号和见解，支持数据驱动的加密货币分析以及未来深度学习的改进。
英文摘要
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.
CommentsThis manuscript has been accepted for presentation at the IEEE International Symposium on Computers and Communications (ISCC 2026)
