Cryptocurrency Insight: A Multiplatform Sentiment Analysis Framework for Price Prediction and Trend Identification

Abstract

Recent volatility in cryptocurrency markets has highlighted the need for more sophisticated prediction models that incorporate market sentiment across multiple platforms. This paper presents a framework for cryptocurrency price prediction and trend identification that leverages natural language processing and machine learning techniques to analyse sentiment across social media platforms, news outlets, and trading forums. Our multi-platform sentiment analysis framework processes data from Twitter, cryptocurrency news websites, and trading platforms to generate comprehensive sentiment indicators. We employed a hybrid approach combining BERT-based sentiment classification with traditional machine learning algorithms to process platform-specific linguistic patterns and domain-specific terminology. The framework was evaluated using historical data from January 2020 to December 2023, focusing on Bitcoin and Ethereum markets. Results demonstrate that our multi-platform approach achieves a 23% improvement in price prediction accuracy compared to single-platform sentiment analysis methods, with particularly strong performance during periods of high market volatility. Furthermore, this research contributes to the growing field of cryptocurrency market analysis by highlighting the importance of cross-platform sentiment aggregation and providing a scalable framework for real-time market sentiment analysis.