Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
2026-07-16 • Machine Learning
Machine LearningComputational Engineering, Finance, and Science
AI summaryⓘ
The authors studied how people feel about the Bitcoin market by looking at data from Bitcoin transactions, its price history, and tweets about Bitcoin. Instead of trying to predict prices, they focused on explaining market sentiment, or how positive or negative people feel. They combined all these data into one set and tested different machine learning methods, finding that XGBoost worked best for classifying sentiment. They also used a tool called SHAP to understand which Bitcoin transaction features influenced their model’s decisions. Their approach helps make sense of cryptocurrency market sentiment using multiple data types.
Bitcoinmarket sentimenton-chain dataTwitter sentimentmachine learningXGBoostSHAPcryptocurrency analysisF1-scorecross-validation
Authors
Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri, Eduardo Borges, Bruno L. Dalmazo
Abstract
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.