Design and Development of Share Price Prediction Framework Using Artificial Intelligence in Fusion with Financial News

Using sentiment data to analyze the stock market fascinates many people. Algorithms for natural language processing can glean information about market sentiment from texts like headlines. This project aims to extract sentiment scores from news articles related to the financial market to predict the future movement of the Standard & Poor's 500 stock market index. Advanced BERT models, short for bidirectional encoder representations from transformers, enable superior sentiment categorization performance. To forecast the sentiment ratings of news items from trustworthy financial news sources, trained beforehand networks of transformers are tweaked using a tagged dataset of financial documents. A comparison is made between the stock market index's time-series historical data and the sentiment ratings derived for the news stories' titles, text, and combined headline content. Expected emotion scores can be used as features in a basic random forest classifier strategy to predict how the index of shares will move. It is clear from the data that sentiment scores derived from news articles are superior for gauging future stock price movements.