Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier
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 marke
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: berri”
- LinkedLinked via arxiv author · 85%Arthur G. Bubolz →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”
- LinkedLinked via arxiv author · 85%Abreu Quevedo →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”
- LinkedLinked via arxiv author · 85%Giancarlo Lucca →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”
- LinkedLinked via arxiv author · 85%Rafael A. Berri →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”
- LinkedLinked via arxiv author · 85%Eduardo Borges →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”
- LinkedLinked via arxiv author · 85%Bruno L. Dalmazo →
“Decoding Market Emotion from Blockchain Activity: A Data-Driven Sentiment Classifier”