ArXiv

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

2026-07-16 · 17:52 UTC ·Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca... ·1 min read
Authors
Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca...
Categories
cs.LG, cs.CE
arXiv
https://arxiv.org/abs/2607.15258v1
PDF
https://arxiv.org/pdf/2607.15258v1

Brief

Decoding Market Emotion introduces a data-driven classifier that explains Bitcoin market sentiment by combining on-chain transactions, historical price data, and daily Twitter sentiment labels. The authors test multiple ML models and find XGBoost most reliable (avg. F1 ≈ 0.84 via cross-validation). They apply SHAP for feature-level interpretability, emphasizing explanation over price prediction and demonstrating meaningful signals for crypto market analysis.

Why it matters

XGBoost was the best-performing classifier in the study, achieving an average F1-score of about 0.84 for daily Twitter-based Bitcoin sentiment labels using cross-validation (paper published to arXiv 2026-07-16).

Key details

  • The model input merged on-chain transaction metrics, historical Bitcoin prices, and Twitter sentiment; SHAP was used to quantify on-chain feature contributions and the dataset was normalized for integrated analysis (accepted to ISCC 2026).
Source evidence

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.

Comment: This manuscript has been accepted for presentation at the IEEE International Symposium on Computers and Communications (ISCC 2026)