Sentiment-Driven Stock Market Prediction with CNN-LSTM

this study explores the impact of sentiment analysis on stock market forecasting by integrating sentiment data with traditional time series forecasting models. Using stock data as a case study, the research employs ARIMA as a baseline model and a hybrid CNN-LSTM model to evaluate the influence of sentiment analysis on prediction accuracy. The findings reveal that sentiment analysis significantly enhances the accuracy of stock market forecasts, particularly in capturing sentiment-driven market movements. This research highlights the potential of combining sentiment analysis with advanced machine learning techniques for more accurate financial predictions.