Predicting Cryptocurrency Prices with Sentiment Analysis and Deep Learning Models

Predicting the price of cryptocurrencies has been a struggle as a result of their high volatility and endless distractions such as social media, market movements, or technical analysis. This document is proposing a novel quite multi-tiered framework combining long-term and short-term market dynamics in a modern age of advanced natural language processing (NLP) and deep learning techniques, where in the feature preparation phase uses a pre-trained financial text model, the FinBERT, to extract sentiment features from platforms like Twitter and Telegram. Hybrid deep learning models that adopt transformer-based architectures efficiently capture sequential dependencies and market fluctuations. It contains the dynamic attention-based weighting mechanism, such that, based on underlying risks with respect to market conditions, adaptively prioritizes a feature. With the utilization of the above features, the short-term and long-term prediction qualities of cryptocurrency prices are boosted, providing a comprehensive understanding in as much as cryptocurrency market behaviors are concerned. Experimental results have shown strong improvements over the baseline models, thus clearly demonstrating the capacities of combining both sentiment analysis and deep learning. This framework thereby would transit traders and investors to more robust forecast things via cutting-edge NLP and deep learning technologies to earn worthwhile market insights.