Predicting Stock Market Trends using Sentiment Analysis on News and Social Media

This research will analyze the efficiency of using sentiment analysis on news articles and social media to predict stock market trends. By analyzing public sentiment from sthis study'sces like Twitter, Reddit, and major financial news platforms, this research will try to capture the psychological influence that the market has on stock prices. Work use Natural Language Processing (NLP) techniques to label textual data as positive, negative, or neutral and aggregate sentiment scores over specific time intervals. These scores are added to the machine learning model: logistic regression, random forest, and Long Short-Term Memory (LSTM). Thus, the proposed approach along with the developed model is tested for determining its predictability pothis researchr when historical data are involved using various evaluation measures for quantifying its predictive pothis researchr after being trained and validated, showing very good and reliable results with better precision obtained by the integration of features based on sentiment analysis. This method brings out the strength of sentiment analysis as an ancillary tool for financial prediction. Investors can gain good knowledge about market trends by tracking the dynamics of public sentiments.