Harnessing Sentiment Analysis and Deep Learning for Precise Stock Price Forecasts

Stock market prediction has remained the subject of inquiry for a long time. Research intensified and new avenues were offered by the advent of computers and machine learning. By using easily accessible historical pricing and sentiment data, this research effort seeks to forecast stock share movements. The models were utilized: the GRU LSTM model that utilized past prices as its independent variable, and a Random Forest model that integrated sentiment analysis data obtained from Intensity Analyzer with other significant variables like the price of gold and oil, currency exchange rates, and policies of the Indian government. Securities returns were added for accuracy purposes. Predictions on the values of HDFC Bank, TCS, SBI-main, and four other shares were the final result. The models' effectiveness was evaluated using the RMSE metric.