Paper Overview
Field: Machine Learning Authors: Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri, Eduardo Borges, Bruno L. Dalmazo Published: 2026-07-16 arXiv: 2607.15258
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 transaction (on-chain) data
- Historical Bitcoin price data
- Daily Twitter sentiment classifications
- Best model: Gradient Boosting (XGBoost) emerged as the most reliable model for classifying sentiment, achieving an average F1 score of ~0.84.
- Interpretability: SHAP (SHapley Additive exPlanations), a game-theoretic model explainability method, was used to quantify the contribution of on-chain features to model predictions, improving transparency.
- Conclusion: The combination of blockchain, financial, and social media data produces meaningful predictive signals and insights, supporting data-driven cryptocurrency analysis and providing a foundation for future deep learning improvements.
- Paper: https://arxiv.org/abs/2607.15258
The method merges sentiment trends with on-chain and financial metrics, normalized into a unified dataset for detailed market analysis. Multiple machine learning models were tested using cross-validation.