NLP Project : sentiment analysis on stock market

   Developing an effective sentiment analysis model for stock market by natural language processing techniques and analyzing news articles from reputable financial sources, the study seeks to explore the relationship between news sentiment and market performance.

 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.

1. Sentiment Analysis using Logistic Regression: For sentiment analysis of the stock market using logistic regression, we followed a structured approach: Data Collection : Textual data related to the stock market was collected from various articles published on financial news websites and journals. These articles provided valuable insights into market sentiment, capturing opinions, analyses, and discussions surrounding specific stocks or broader market trends. Data Preprocessing : The textual data extracted from articles underwent rigorous preprocessing to ensure its suitability for sentiment analysis. This involved cleaning the text by removing special characters, punctuation, and stopwords. Additionally, the text was tokenized and transformed into numerical features using techniques like TF-IDF (Term Frequency-Inverse Document Frequency). Model Training : The preprocessed data was split into training and testing sets. The logistic regression model was trained on the labeled training

 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

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.