This project is a Python-based sentiment analysis tool that analyzes restaurant reviews to gauge customer satisfaction. It utilizes Natural Language Processing (NLP) techniques and a Naive Bayes Classifier to classify reviews into positive, negative, or neutral sentiments.
- Data collection: Gathered a diverse dataset of restaurant reviews from various sources.
- Data preprocessing: Cleaned and prepared the data for analysis.
- Sentiment analysis: Developed a Naive Bayes Classifier model to classify reviews.
- Accuracy: Achieved an accuracy rate of over 91% in sentiment classification.
To get started with this project, follow these steps:
-
Clone the repository:
git clone https://github.com/MohamedKhalifa1/restaurant-review-sentiment-analysis.git -
Install the required dependencies:
pip install -r requirements.txt -
Run the analysis:
python sentiment_analysis.py
- After running the analysis script, you will receive sentiment analysis results for your restaurant reviews.
- Use the insights to improve customer satisfaction and make data-driven decisions.
If you'd like to contribute to this project, please follow these guidelines:
- Fork the project on GitHub.
- Create a new branch for your feature or bug fix.
- Make your changes and submit a pull request.
- Add real-time data analysis capabilities.
- Develop a user-friendly web interface.
- Collaborate with local restaurants for personalized recommendations.
This project is licensed under the MIT License - see the LICENSE file for details.
- Special thanks to the open-source NLP community for providing valuable resources and libraries.
- Gratitude to the contributors and supporters of this project.
If you have any questions or suggestions, feel free to reach out to Mohamed Ashraf on LinkedIn.