Predicting Stock Prices with Investor Sentiment and Deep Learning Techniques

A key component of risk assessment, investment strategy, and financial planning is stock price prediction. Although technical indications and historical price patterns are the mainstays of traditional forecasting models, these frequently ignore the emotional and behavioural factors that affect financial markets. This study presents a hybrid methodology that combines investor sentiment gleaned from internet sources including social media and financial news sites with historical stock data. The approach improves the accuracy and responsiveness of predictions by incorporating both numerical trends and psychological cues. The system interprets textual data and produces sentiment scores by combining natural language processing methods with a deep learning architecture. Sentiment-enriched forecasts perform better than traditional models in both short-term trend prediction and market volatility awareness, according to experimental evaluation using real-world data. Practical ramifications for real-time forecasting systems and ethical issues are also covered in this paper. Keywords: Stock price, Investor Sentiment, Price Trend Prediction, LSTM, Financial Time Series, Machine learning