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 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 approximately 0.84.
To enhance transparency, the authors employ SHAP (SHapley Additive exPlanations), a game-theoretic model interpretability method, to quantify the contribution of on-chain features to the model's predictions.
Key Takeaways
- Goal: Explain Bitcoin market sentiment, not predict prices
- Data sources: Blockchain transactions, historical Bitcoin prices, daily Twitter sentiment classifications
- Best model: XGBoost (Gradient Boosting) with average F1 ≈ 0.84
- Explainability: SHAP analysis quantifies on-chain feature contributions
- Conclusion: Combining sentiment with on-chain and financial metrics produces meaningful predictive signals, supporting data-driven crypto analysis and future deep learning extensions