Abstract
Abstract: This research discusses the ever-changing nature of the stock market by using models such as LSTM and ARIMA to forecast stock prices. It recognizes the volatility and unpredictability of financial markets recognizing the need for robust tools that can accurately predict. Sentiment analysis is used for enhancing the accuracy of stock price prediction, drawing information from various sources like news resources and social media platforms that reflect public sentiments and opinions. We aim to analyze a thorough aspect of factors influencing stock prices by combining these sentiments with the models. The LSTM model is used to study long-term dependencies in stock price trends and ARIMA provides information into the time series components. Combining sentiment analysis helps us to scan the emotional tone around a particular stock and contributes valuable data to the prediction process. The integration between the machine learning models and sentiment analysis offers an extensive approach to predicting stock prices considering both past trends and current public sentiment. This study adds to the work in progress to develop more accurate and adaptive tools for exploring the difficulties of the stock market.
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