Cryptocurrency Price Prediction using Sentiment Analysis of Social Media

The rapid growth of cryptocurrency markets has attracted extensive research interest in predicting price movements, yet their high volatility and sensitivity to public opinion make accurate forecasting a persistent challenge.In recent years, sentiment analysis of social media platforms such as Twitter, Reddit, and Telegram has emerged as a valuable approach for understanding market behaviour.This survey paper provides a comprehensive review of existing studies that integrate sentiment analysis with machine learning and deep learning models for cryptocurrency price prediction.It examines various data sources, sentiment extraction techniques, model architectures, and evaluation metrics used across the literature.The paper also highlights how combining social sentiment with traditional financial indicators can enhance prediction accuracy and capture short-term market dynamics.Furthermore, key challenges-such as data noise, manipulation, limited generalization, and real-time applicability-are discussed to identify gaps in current research.Finally, the survey outlines potential future directions, including the use of transformer-based models, multi-modal sentiment fusion, and on-chain behavioural analytics for more robust and interpretable crypto market forecasting systems.