Bitcoin Price Prediction Using Deep Learning and Fuzzy Logic

Abstract

Bitcoin's growing popularity has spurred interest in understanding its price dynamics. This study investigates the relationship between public sentiment towards Bitcoin and its price fluctuations. By analyzing Facebook and Twitter data, we employed a novel approach combining deep learning and fuzzy logic. Sentiment analysis was conducted using multiple lexicons, followed by clustering and classification of reviews into positive, neutral, and negative categories. Subsequently, a two-level fuzzy logic model integrated sentiment data and Bitcoin prices to predict future prices. The proposed methodology outperformed existing models, demonstrating the effectiveness of our approach in capturing the complex interplay between public opinion and Bitcoin price trends.