https://github.com/swish78/autoreview
https://github.com/swish78/autoreview
Last synced: over 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/swish78/autoreview
- Owner: Swish78
- License: mit
- Created: 2024-08-05T14:02:24.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-12T18:31:07.000Z (almost 2 years ago)
- Last Synced: 2024-08-12T21:34:27.173Z (almost 2 years ago)
- Language: Python
- Size: 2.26 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AutoReview - AI-Powered Product Review Aggregator
AutoReview is a web application that aggregates and analyzes product reviews from multiple sources to provide summarized insights and ratings. Leveraging machine learning, it offers a comprehensive view of product sentiment and quality.
## Features
- Aggregates product reviews from various sources (e.g., Amazon, Yelp).
- Analyzes sentiment of reviews (positive, negative, neutral).
- Provides summarized insights and ratings for each product.
- Real-time and periodic updates using cron jobs.
- Interactive charts and graphs for visualizing review data.
## Tech Stack
- **Frontend:** React, Tailwind CSS
- **Backend:** Django, Django REST Framework (DRF)
- **Machine Learning:** Python-based ML models for sentiment analysis and text summarization
- **Database:** PostgreSQL / SQLite
- **Cron Jobs:** Celery
- **Web Scraping / APIs:** BeautifulSoup, Scrapy, or platform APIs
- **Deployment:** AWS, Heroku, Vercel, Netlify
## Installation
### Prerequisites
- Python 3.x
- Node.js and npm
- PostgreSQL / SQLite
### Backend Setup
1. Clone the repository:
```bash
git clone https://github.com/yourusername/autoreview.git
cd autoreview
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install backend dependencies:
```bash
pip install -r requirements.txt
```
4. Set up PostgreSQL and create a database. Update the database settings in `autoreview/settings.py`.
5. Run migrations to set up the database:
```bash
python manage.py migrate
```
6. Set up Celery for periodic tasks:
```bash
celery -A autoreview worker -l info
```
7. Run the Django development server:
```bash
python manage.py runserver
```
### Frontend Setup
1. Navigate to the frontend directory:
```bash
cd frontend
```
2. Install frontend dependencies:
```bash
npm install
```
3. Start the React development server:
```bash
npm start
```
## Configuration
- **Machine Learning Models:** Update paths and configurations for ML models in the `autoreview/ml` directory.
- **Cron Jobs:** Configure Celery tasks for data collection and updates in `autoreview/tasks.py`.
## Usage
1. **Search for Products:** Use the search functionality to find products and view aggregated reviews.
2. **View Insights:** Explore summarized insights and sentiment analysis for each product.
3. **Interactive Charts:** Visualize ratings and sentiment distributions using interactive charts.
## Contributing
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Create a new Pull Request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Acknowledgments
- **Machine Learning Libraries:** Thanks to the open-source libraries used for sentiment analysis and summarization.
- **Frontend Libraries:** React and Tailwind CSS for a responsive and modern UI.
- **Git Cheatsheet:** https://education.github.com/git-cheat-sheet-education.pdf