# Google Review Scraper (Python + Camoufox)

A professional-grade Google Review data extraction system built in Python for market research and business consulting use cases.

This project was developed as part of a client market research engagement for a business consultant, where large volumes of structured customer review data were required for analysis, benchmarking, and strategic decision-making.

Project Use Case

• Market research and customer sentiment analysis
• Competitor benchmarking across multiple businesses
• Business intelligence and consulting reports
• Automated collection of public review data

Key Features

📊 Business-Ready Data Output

• Structured CSV files per target business
• Incremental saving to prevent data loss
• Ready for Excel, Google Sheets, Power BI, or Python analysis

🛡️ Stealth & Reliability

• Uses Camoufox (stealth browser automation) to mimic real user behavior
• Reduces CAPTCHA challenges and automated detection
• Designed for long-running and large-scale scraping sessions

🔄 Dynamic Review Loading

• Automatically handles dynamically loaded review content
• Ensures full review coverage for businesses with high review volume

🍪 Session & Cookie Management

• Supports loading session cookies to:
  • Bypass consent and verification prompts
  • Maintain authenticated sessions
  • Improve scraping stability

🧾 Structured Data Extraction

Extracted fields include:

• Business name and location
• Reviewer name
• Rating
• Review date
• Review text

📜 Logging & Monitoring

• Integrated logging for progress tracking and debugging

Technology Stack

• Python 3.8+
• Camoufox (Playwright-based stealth automation)
• Playwright-compatible browser engines
• CSV-based data pipelines

Installation

1. Install dependencies

   pip install camoufox

2. Install browser binaries

   python -m camoufox fetch

Configuration

Input URLs (input.txt)

Add Google review listing URLs (one per line):

https://www.google.com/...
https://www.google.com/...

Cookies (cookies.json) – Recommended

Using session cookies improves stability and reduces interruptions.

1. Log in to Google in a regular browser
2. Export cookies for .google.com
3. Save as cookies.json in the project directory

Usage

Run the scraper:

python google_review_scraper.py

The browser launches (non-headless by default), loads reviews dynamically, and exports structured data automatically.

Output

CSV files: review_list_1.csv, review_list_2.csv, ... Each file corresponds to a URL from input.txt.

Professional Context

• Built as part of a real client market research project
• Client identity and proprietary data are intentionally excluded
• Demonstrates real-world experience with:
  • Stealth scraping
  • Market research automation
  • Reliable data extraction pipelines

Disclaimer

This project is shared as previous professional experience. Users are responsible for ensuring compliance with website Terms of Service and applicable regulations when using this code.