Harnessing Textual Emotions and ANFIS for Accurate Stock Market Forecasting

In this study introduces a novel framework for stock market prediction that combines the sentiment analysis with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Sentiment analysis is employed to extract public sentiment trends from financial news, and social media to get classifying them into positive, negative, or neutral categories. These sentiment scores, are combined with historical stock market data, apply into the ANFIS model, which blends the neural network learning with fuzzy logic interpretability. The suggested system captures the nonlinear links between sentiment and stock price changes, resulting in accurate forecasting. According to the experimental data, the hybrid model is a classic machine learning techniques on the basis of prediction accuracy. This research demonstrates the sentiment into computational model, and the way for financial forecasting and decision making.