Bitcoin Sentiment Analysis using Machine Learning Algorithms
Adit Vakil1, Manavi Jain2, Shweta Joshi3, Prof. Abhilasha Raghtate4
1U.G. Student, Department of Electronics and Telecommunication, Dwarkadas J. Sanghvi College of Engineering, Vile Parle (W), Mumbai, 400056, Maharashtra, India.
2 U.G. Student, Department of Electronics and Telecommunication, Dwarkadas J. Sanghvi College of Engineering, Vile Parle (W), Mumbai, 400056, Maharashtra, India.
3 U.G. Student, Department of Electronics and Telecommunication, Dwarkadas J. Sanghvi College of Engineering, Vile Parle (W), Mumbai, 400056, Maharashtra, India.
4 U.G. Assistant Professor, Department of Electronics and Telecommunication, Dwarkadas J. Sanghvi College of Engineering, Vile Parle (W), Mumbai, 400056, Maharashtra, India.
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Abstract - The rapid rise of cryptocurrencies, particularly Bitcoin, has intensified research interest in understanding the factors influencing their price movements. Among these factors, social media sentiment has emerged as a crucial predictor, reflecting collective investor mood and market expectations. This paper provides a comprehensive survey of various sentiment analysis models applied in cryptocurrency markets, with a specific focus on the relationship between social media sentiment and Bitcoin price fluctuations. The study identifies the Aigents model as the most effective, showing significant improvements in predictive accuracy following fine-tuning. Findings reveal a predictive association between sentiment measures and price changes, typically with a latency of one to two days. The paper offers insights into the capabilities and limitations of existing Natural Language Processing (NLP) models in the context of cryptocurrency sentiment analysis, presenting practical implications for investors and analysts in navigating the volatile cryptocurrency markets.
Key Words: cryptocurrency, Bitcoin, social media sentiment, natural language processing (NLP), sentiment analysis, price prediction, machine learning models, Aigents model, financial forecasting, Twitter, Reddit, artificial neural networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Naive Bayes, Extreme Gradient Boosting (XGBoost), predictive analytics, market behavior, interpretable AI.
