Incorporating Investor Sentiment: A Stock Price Prediction Model with Sparrow Search Optimization and LSTM

This study shows a new way to predict stock prices by using both the feelings of investors and advanced computer techniques. This research aims to create a model that can predict how investors feel about the market, so we can make better predictions about how the market will change. This new way of doing things combines sentiment analysis with LSTM networks and Sparrow Search Optimization to tune the parameters, giving a complete view of how the market behaves. The plan includes gathering data from different places, getting it ready, and making a design for a special kind of computer system that includes feelings. Sparrow Search Optimization is used to make a model work better. The experiment showed that the new model is better at predicting stock prices than the old ways. The error in the predictions was reduced by 20%. This shows how important it is to analyze people's feelings when predicting finances. The research ends with advice for people who invest money, ideas for future research, and the overall impact of using sentiment analysis and machine learning in financial analysis.