Stock Sentiment Analysis using News Headlines
Description Stock Sentiment Analysis using News Headlines is a project that aims to predict the sentiment (positive, negative, or neutral) of stock prices based on news headlines. The sentiment analysis helps investors and traders make informed decisions by understanding how the news is impacting the stock market.
The project leverages natural language processing (NLP) techniques to process and analyze the textual data from news headlines. By extracting key features and sentiment from the headlines, the algorithm provides a sentiment score for each stock, indicating whether the overall sentiment is bullish, bearish, or neutral.
Features Utilizes a dataset of news headlines related to various stocks and financial events. Employs NLP techniques for text preprocessing, tokenization, and feature extraction. Implements a machine learning algorithm to classify the sentiment of news headlines. Provides a sentiment score for each stock to indicate the overall market sentiment. Requirements To run this project locally or on your own machine, you will need the following:
Python (>=3.6) Jupyter Notebook or any Python IDE Libraries: Pandas, NumPy, NLTK, scikit-learn, etc. (requirements specified in requirements.txt) Getting Started Clone the repository to your local machine. Install the required libraries by running pip install -r requirements.txt. Download the news headlines dataset (provide the link to the dataset or instructions on how to obtain it). Open the Jupyter Notebook or Python IDE to run the stock_sentiment_analysis.ipynb file. Follow the step-by-step instructions in the notebook to preprocess data, build the model, and analyze the sentiment. Dataset The dataset used in this project contains historical news headlines related to various stocks. Each headline is labeled with the corresponding stock's movement (e.g., "up," "down," or "unchanged"). The dataset is divided into training and testing sets to evaluate the model's performance accurately.
Please note that the dataset is for educational purposes only and may not reflect real-world market conditions.
License Specify the license (e.g., MIT, Apache 2.0) under which the project is released.
Acknowledgments List any references, articles, or other projects that inspired or influenced your work.
Contributing Outline how others can contribute to your project (if applicable).
Contact Provide your contact information (amitrathore110409@gmail.com) for users to get in touch with you for questions or feedback.
Data Description The dataset used for Stock Sentiment Analysis typically consists of two main components:
Historical Financial Data:
Stock Prices: Daily or intraday historical stock price data for various companies or stocks. It includes information such as opening price, closing price, highest price, lowest price, and trading volume. Financial Indicators: Relevant financial indicators like market capitalization, price-to-earnings (P/E) ratio, earnings per share (EPS), etc. Market Index Data: Information about the performance of broader market indices like S&P 500, NASDAQ, etc. News Headlines:
Textual data containing news headlines or articles related to specific companies or the overall market. News sentiment labels: Each headline is typically labeled with a sentiment tag, such as "positive," "negative," or "neutral," indicating the sentiment associated with the news. The data may cover various time periods, ranging from a few months to several years, depending on the specific use case.
Data Sources Stock Sentiment Analysis datasets can be obtained from various sources:
Financial APIs: APIs from financial data providers that offer historical stock prices, financial indicators, and market index data. News APIs: APIs that provide access to news headlines and their corresponding sentiment labels