Impact of Natural Language Processing on Sentiment Analysis in Stock Market Predictions

This study explores the connections between sentiment analysis and Natural Language Processing techniques to enhance stock market predictions, specifically targeting India's Nifty 50 index. Historical price data serves as the basis for traditional stock prediction models; however, they often overlook the significant impact of market sentiment, as highlighted in financial news articles. Not including these details may lead to projecting false predictions as since stock values are very sensitive to market responses toward news events. This research has extracted measures of sentiment that outline the polarity and subjectivity from a judiciously selected collection of news articles related to Nifty. Combining all these indicators with the stock market history makes the predictions highly accurate. Our results were different from the traditional ML models with an MSE of 0.0058 and an RMSE of 0.0762 using Long Short-Term Memory. The findings when including sentiment improves the accuracy in making predictions as well as provides a better insight into market behavior, which in turn helps analysts and investors make better choices in the volatile stock markets.