Finsen is a financial news sentiment analysis model written in Python. It's a simple model that is trained on headlines of financial news articles from the internet. It works by calculating the polarity of the news headlines and then classifying them into positive, negative or neutral.
- 📰 Analyze financial news articles from various sources
- 🔍 Extract key information and sentiment from news content
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Clone the repository:
git clone https://github.com/root-Manas/finsen.git cd finsen -
Create a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate`
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Install the required packages:
pip install -r models/requirements.txt
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Ensure you're in the project directory and your virtual environment is activated (if used).
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Run the Jupyter Notebook:
jupyter notebook models/Finsen.ipynb
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Follow the instructions in the notebook to analyze financial news and generate insights.
Finsen relies on several Python packages to function. The main dependencies include:
- pandas
- numpy
- matplotlib
- scikit-learn
- gradio
For a complete list of dependencies, please refer to the models/requirements.txt file.
Here are some example outputs from the Finsen model:
We welcome contributions to Finsen! If you have suggestions for improvements or encounter any issues, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Thanks to all the open-source libraries that made this project possible.