Paper Overview
- Research Area: Machine Learning (ML)
- Authors: Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri, Eduardo Borges, Bruno L. Dalmazo
- Published: 2026-07-16
- arXiv: 2607.15258
- Merge sentiment trends with on-chain and financial metrics, normalized into a unified dataset for detailed market analysis.
- Test multiple machine learning models using cross-validation.
- Apply SHAP (SHapley Additive exPlanations), a game-theoretic explainability method, to quantify how on-chain features contribute to model predictions.
- Gradient Boosting (XGBoost) emerged as the most reliable model for classifying sentiment, achieving an average F1 score of ~0.84.
- SHAP analysis improved transparency by revealing the contribution of individual on-chain features.
- The combination of blockchain, financial, and social media data produces meaningful predictive signals and insights.
Summary
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.
Methodology
Key Findings
Conclusion
The study demonstrates that combining on-chain activity with financial metrics and social media sentiment can effectively explain Bitcoin market emotion, supporting data-driven cryptocurrency analysis and laying groundwork for future deep learning improvements.
--- *Auto-collected on 2026-07-18*