Stock Market Trend Analysis and Prediction Using Sentiment Analysis

The intricacy of financial markets has made stock market prediction a difficult task. Conventional techniques often depend on historical data, technical analysis, and fundamental research; however, these methods could better capture the market dynamics impacted by several outside influences. Sentiment analysis, a subfield of natural language processing (NLP), has emerged as a practical approach for incorporating unconventional data sources, such as news articles, market reports, and social media, into stock market prediction models. This work suggests a novel method for predicting changes in the stock market by utilizing textual data sentiment analysis. The approach entails gathering and preparing textual data from several sources, applying natural language processing (NLP) techniques to extract sentiment features, and incorporating these features into machine learning models for prediction. The proposed approach's efficiency is assessed using sentiment data from news articles, social media sites, and historical stock market data. The results demonstrate that adding sentiment research to stock market projections can improve their accuracy compared to traditional methods. The study's conclusion discusses the implications of sentiment analysis in stock market prediction and the potential for further research in this area.