Amazon Fake Review Impact Analysis

Project Structure

project/
├── scraping/
│   ├── amazon_reviews_scraper.ipynb    
│   └── selectors.yml                   
├── data/
│   ├── books_category_reviews.csv
│   └── beauty_category_reviews.csv
├── analysis/
│   └── analysis.ipynb
└── requirements.txt

Features

• Web scraping of Amazon product reviews
• Text preprocessing and cleaning
• Fake review detection using machine learning
• Comparative analysis between product categories
• Statistical analysis and visualization
• Sentiment analysis
• Pattern detection in review behaviors

Setup

Step 1: Clone the repository

git clone https://github.com/yourusername/Amazon_fake_review_impact_analysis.git
cd Amazon_fake_review_impact_analysis

Step 2: Create and activate a virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Step 3: Install dependencies

pip install -r requirements.txt

Usage

Data Collection

1. Open amazon_reviews_scraper.ipynb
2. Update product URLs in the notebook
3. Run all cells to collect reviews
4. Scraped data will be saved in data

Analysis

1. Open analysis.ipynb
2. Run the notebook to:
   • Process the collected reviews
   • Detect potential fake reviews
   • Generate visualizations
   • View comparative statistics

Key Findings

The analysis provides insights into:

• Fake review patterns across categories
• Impact on product ratings
• Sentiment distribution
• Review length and content patterns
• Temporal patterns in review posting

Tools & Technologies

• Python: 3.9+
• Jupyter Notebooks
• Key Libraries:
  • pandas
  • scikit-learn
  • NLTK
  • TextBlob
  • Matplotlib/Seaborn
  • Requests
  • Selectorlib

Notes

• Respect Amazon's robots.txt and rate limiting
• Results are probabilistic and should be interpreted accordingly
• The model's accuracy depends on training data quality
• Regular updates may be needed as review patterns evolve

License

This project is licensed under the MIT License - see the MIT License file for details.

Disclaimer

This Amazon scraper is intended for educational purposes only. Please ensure that your use of this tool complies with Amazon's terms of service and any applicable laws and regulations.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Open a pull request