Sentiment-Infused Stock Price Prediction: A Hybrid Model for Capturing Market Sentiments and Financial Trends

Sentiment-augmented stock price prediction suggests a real-time prediction framework from the stock market which joins deep learning combinations with sentiment analysis. The proposed fusion architecture integrates Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks in conjunction with traditional machine learning models like XGBoost and uses these to record and analyze market activities in real-time under high frequency. Ensembles answer to the volatility of the market and weaknesses in data by offering predictions model with a short turnaround to relieve the problem of price movements. Real-time data on the market, such as market data and live news, will introduce the system to more clues. The focus adopted here provided a way for more accurate forecasting, such as higher probability insights for practically possible trading. Such outcomes might showcase the power of their combined model in detecting rather intricate patterns of prices.