Conclusion:

Integrating logistic regression and LSTM networks for sentiment analysis and stock market prediction offers a powerful approach that combines the best of both worlds. Logistic regression provides clarity and interpretability, making it suitable for initial sentiment classification and feature extraction. LSTM networks bring in advanced temporal analysis capabilities, essential for capturing the intricate patterns in sentiment data over time. Together, they provide a robust, accurate, and comprehensive tool for predicting stock market movements based on sentiment analysis, offering significant advantages in risk management, investment strategy formulation, and gaining a competitive edge in the financial markets.

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Result :

 By using the yahoo finance dataset  we were able to sentiment classification and predict the correct sentiment(positive or negative).The model we have use is there is logistic regression. Now as the dataset was not having any features (as it was just sentences).it was not possible to obtain the sentence using any machine learning model so we have collected the dataset of various stocks which were having features of closing price and opening price and therefore we have were able to do prediction that particular dataset using lstm mechanism
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Motivation:

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 Sentiment analysis on the stock market is a valuable tool for investors, analysts, and financial institutions. It provides insights that are not available through traditional analysis, helping to predict market movements, manage risks, and create more informed investment strategies. As technology continues to evolve, the accuracy and utility of sentiment analysis are likely to increase, further enhancing its importance in the financial sector.
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