Utilizing Sentiment Analysis and Machine Learning to Forecast Stock Price Changes from Financial News

This study looks into the use of sentiment analysis to forecast stock prices by using headlines from the day's financial news to foretell changes in the market. The research creates a prediction model that can anticipate whether stock prices will rise or fall based on sentiment gleaned from news stories by combining Natural Language Processing (NLP) with machine learning. CountVectorizer is used for preprocessing, tokenization, and feature extraction on a dataset of daily headlines and stock results (positive or negative). Using a Random Forest Classifier, high precision and recall metrics were attained along with an accuracy of 85.97%. The model performs well in forecasting rises in stock price, indicating the influence of market mood on stock behavior. In order to conduct a more thorough contextual analysis, future work will involve extending the model to include historical stock data, macroeconomic variables, and sophisticated NLP approaches like BERT. The present study underscores the capacity of sentiment analysis to augment financial market forecasting and decision-making.