Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
For cryptocurrency analysts and investors, this provides a transparent, data-driven method to interpret market sentiment, though it is incremental as it applies existing techniques to a new domain.
This work presents a sentiment classifier for Bitcoin that combines on-chain data, financial metrics, and Twitter sentiment, achieving an average F1-score of 0.84 using XGBoost. SHAP analysis improves model interpretability by quantifying feature contributions.
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.