This project aims to demonstrate the implementation of logistic regression for sentiment analysis. By analyzing text data, we develop a model capable of classifying sentiments as either positive or negative. This notebook provides a step-by-step guide through the process, including data preprocessing, model training, and evaluation.
- Data Preprocessing: Details on how the data is cleaned and prepared for modeling, including tokenization and vectorization.
- Model Implementation: An overview of logistic regression and its application in sentiment analysis.
- Evaluation: Metrics used to evaluate the model's performance, including accuracy.
- Python 3.x
- Jupyter Notebook
- Clone this repository to your local machine.
- Navigate to the repository's directory in a terminal or command prompt.
- Run Jupyter Notebook and open `sentiment_analysis_logistic_regression.ipynb.`
- Execute the notebook cells sequentially to reproduce the analysis.
Contributions to improve the project are welcome. Please follow these steps:
- Fork the repository.
- Create a new branch (`git checkout -b feature/AmazingFeature`).
- Commit your changes (`git commit -m 'Add some AmazingFeature`).
- Push to the branch (`git push origin feature/AmazingFeature`).
- Open a pull request.
Distributed under the MIT License. See `LICENSE` for more information.
Project Link: https://github.com/Musa24/sentiment_analysis_logistic_regression