[论文] Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentim...

论文概要

研究领域: ML
作者: Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri, Eduardo Borges, Bruno L. Dalmazo
发布时间: 2026-07-16
arXiv: 2607.15258

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长，引发了对其市场行为的强烈关注。本研究提出一种新方法，通过结合链上和金融数据与社交媒体帖子来分析比特币市场情绪。与旨在预测价格的模型不同，本工作专注于使用区块链交易、比特币历史价格数据和每日Twitter情感分类来解释市场情绪。该方法将情感趋势与链上和金融指标融合，归一化为数据集进行详细的市场分析。使用交叉验证测试了多种机器学习模型，梯度提升（XGBoost）成为情感分类最可靠的模型，平均F1分数约为0.84。基于博弈论的模型可解释性方法SHAP（SHapley Additive exPlanations）被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洞察，支持数据驱动的加密货币分析和未来深度学习改进。

原文摘要

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...

自动采集于 2026-07-18

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