Published Paper Details:

The goal of this project is to increase the accuracy of stock market predictions by combining machine learning techniques with sentiment analysis. This study examines the combination of sentiment analysis and machine learning approaches to increase the accuracy of stock market predictions, making use of the enormous volumes of textual data that are available from several sources, such as social media and financial news. We use sophisticated machine learning techniques to use the large volumes of textual data that are available from several sources, such as social media and financial news, in addition to traditional numerical data. By capturing the complex relationship between market sentiment and quantitative data, our method seeks to improve the predictive power of our models.We show how well our methodology works for precisely predicting changes in the stock market through extensive data preparation, feature engineering, and model construction. Sentiment analysis integration offers insightful information on the dynamics of market sentiment, giving analysts and investors a more comprehensive grasp of market behavior. The field of stock market prediction is advanced by this research by utilizing cutting-edge methods that integrate textual and numerical data analysis.