Integration of natural language processing and machine learning for stock market sentiment analysis

Identifying stock market patterns is challenging, but essential in the field of financial analysis. This research examines the effects of combining sentiment analysis with the time series models when predicting stock price movements using the sliding window approach. Incorporation of sentiment data in our empirical analysis of ten companies reduces the MSE, MAE, and RMSE by a large percentage as compared with the models without sentiment data. Among the two firms, JP Morgan and Microsoft, they benefited most from the use of sentiment integration, which is critical in volatile markets. The outcome also explain how expanding the sliding window length has a positive effect on the forecast accuracy. But still these progresses are still facing some issues such as noisy sentiment data and scalability. The present research advances essential findings for the use of sentiment for the financial context and lays the groundwork for the subsequent developments in the mixed-viewpoint modeling for prediction.