A Stock Price Prediction Model Based on Investor Sentiment and Optimized Deep Learning

Stock price prediction remains a challenging problem due to the highly volatile nature of financial markets. Traditional forecasting methods primarily rely on historical price data and technical indicators, often failing to account for the psychological and behavioral aspects influencing market movements. Recently, investor sentiment analysis has emerged as a crucial factor in stock price forecasting, leveraging opinions and emotions expressed on social media, news articles, and financial reports. This study presents a novel deep learning-based stock price prediction model that integrates investor sentiment with optimized neural networks. Sentiment data is extracted using Natural Language Processing (NLP) techniques and combined with historical market data to enhance prediction accuracy. We employ an optimized deep learning framework incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to capture temporal and spatial patterns in financial datasets. Hyperparameter tuning and attention mechanisms further refine the model's predictive performance. Extensive experiments on real-world stock market data demonstrate that our proposed model outperforms traditional machine learning techniques and standalone deep learning models. The results indicate a significant improvement in forecasting accuracy, reducing mean squared error (MSE) and increasing correlation with actual stock trends. This research contributes to financial technology by integrating investor sentiment analysis with optimized deep learning architectures. The findings highlight the potential of sentiment-aware models in guiding investment decisions and risk management strategies. Future work will explore advanced reinforcement learning techniques to further enhance prediction robustness.