Facilitating Stock Recommendations through Sentiment Analysis

Sentiment analysis is a relatively new method of stock recommendation that assesses news articles, social media feeds, and other information sources to ascertain investor sentiment towards a particular stock using machine learning and natural language processing. The model suggests whether to buy, hold, or sell the stock based on sentiment analysis. By emphasizing trends and patterns in investor sentiment, the objective is to give investors insightful information that can help their decision-making. Several methods, including LSTM, Decision Trees, Random Forests, Logistic Regression, and Gradient Boosting, were implemented to find the most accurate sentiment analysis model. With an accuracy score of 85.02% among all, the Random Forest model came out as the most appropriate.