Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier

2026-07-16

AI Summary

This study addresses the challenge of deciphering Bitcoin market sentiment from multi-source heterogeneous data rather than directly forecasting prices. By integrating on-chain metrics, historical price data, and daily Twitter sentiment classifications, the authors construct a normalized dataset and employ an XGBoost model for sentiment classification. The work presents the first systematic fusion of on-chain, financial, and social media data specifically for sentiment interpretation in cryptocurrency markets. To enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis is incorporated, revealing the pivotal role of on-chain features in sentiment determination. Experimental results demonstrate that the proposed approach achieves an average F1 score of approximately 0.84 in sentiment classification, thereby validating the efficacy and analytical insight gained through multi-source data integration in cryptocurrency sentiment analysis.

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.

Problem

• market sentiment
• blockchain activity
• Bitcoin
• social media
• on-chain data

Innovation

• on-chain data
• market sentiment classification
• XGBoost
• SHAP interpretability
• multimodal data fusion