Sentiment Analyis and Bitcoin Price Prediction

Abstract

Abstract The emergence of Bitcoin as a decentralized digital currency has underscored the importance of developing advanced techniques for predicting its price fluctuations. This study evaluates the predictive power of Bitcoin-related Google search volumes and Twitter sentiment analysis within short time frames. By leveraging machine learning algorithms and opinion mining, we identify correlations between online behaviors and Bitcoin price movements. Our methodology encompasses data sourcing, preprocessing, exploratory analysis, feature selection using Correlation Analysis, F-regression, Shapley values, and price prediction with a Long Short-Term Memory (LSTM) model. Findings reveal that Google search data, compared to Twitter sentiment, significantly enhances model accuracy and reduces prediction errors. The study suggests future research to investigate other search engines and online news sentiment, acknowledging limitations in data quality and accessibility of historical Twitter data.