A Robust ML-Based Framework for Cryptocurrency Price Forecasting with Sentiment-Driven Alerts

This work introduces a new machine-learningbased cryptocurrency price prediction architecture through the integration of historical prices and real-time sentiment analytics. The model utilizes Natural Language Processing (NLP) methods for sentiment extraction and quantification of market mood from social media sites like Twitter and Reddit. Sentiment data are preprocessed and converted to numerical features describing trends in public opinion. These features are combined with historical market indicators and input into a machine learning pipeline that determines trends affecting price movements. A real-time decision engine processes these outcomes in order to create automated buy and sell orders. The algorithm is implemented in MATLAB and consists of data ingestion modules, sentiment scoring, feature combination, prediction, and automated trading decisions. The operational flow allows for rapid and responsive reaction to market dynamics, hence ensuring risk minimization while profit maximization. The main novelty of this research is its fusion of real-time sentiment analytics with predictive modeling, hence offering a practical and intelligent trading assistant to cryptocurrency traders.