Stock Market Price Prediction Using Sentiment Analysis

Abstract

The domain of stock market price forecasting has experienced a significant transformation with the integration of sentiment analysis methods. This study explores the application of the XGBoost algorithm, a robust gradient boosting technique, in the context of stock price prediction enhanced by sentiment analysis. The research leverages historical stock market data and sentiment data from diverse textual sources, including news articles, social media, and financial reports. It encompasses data preprocessing, sentiment analysis, and the integration of sentiment scores with stock data, which serves as the feature set for the XGBoost model. Hyperparameter tuning and cross-validation are used to enhance the model's performance with rigorous evaluation metrics providing insight into its predictive accuracy. The XGBoost algorithm, known for its versatility and predictive power, is revealed as a potent tool in forecasting stock prices, offering the potential for more informed investment decisions. This study serves as an exploration of the fusion between cutting-edge machine learning and the financial world, shedding light on the evolving landscape of stock market price prediction and the substantial role of sentiment analysis coupled with XGBoost in enhancing prediction accuracy.