Product Review Aggregator

Scrape and aggregate product reviews from Amazon, Walmart, and Target into a unified JSON format. Built with Playwright for robust handling of JS-heavy, anti-bot-protected e-commerce sites.

Use Cases

• Brand Monitoring — Track what customers say about your products across multiple retailers from a single data source.
• Competitive Analysis — Compare review sentiment and ratings for competitor products on the same platforms.
• Product Research — Aggregate feedback to identify common complaints, feature requests, and satisfaction trends.
• Market Intelligence — Feed unified review data into dashboards, NLP pipelines, or alerting systems.

Input

Field	Type	Required	Default	Description
platform	Select	✅	—	amazon, walmart, or target
productUrl	String	—	—	Direct product page URL (takes priority over search)
searchQuery	String	—	—	Search term to find the product
maxReviews	Integer	—	50	Max reviews to scrape (1–200)
minRating	Select	—	0	Filter by minimum star rating (0 = all)
sortBy	Select	—	most_relevant	Sort order for reviews
proxyConfiguration	Proxy	—	—	Apify proxy settings (recommended)

Note: Either productUrl or searchQuery must be provided.

Output

Every review is emitted as a single dataset item with a unidentical schema regardless of source platform:

{
  "productTitle": "Apple AirPods Pro (2nd Generation)",
  "productUrl": "https://www.amazon.com/dp/B0D1XD1ZV3",
  "platform": "amazon",
  "reviewerName": "Jane D.",
  "rating": 5,
  "reviewTitle": "Best earbuds I've ever owned",
  "reviewBody": "The noise cancellation is incredible and the sound quality blows me away every time...",
  "reviewDate": "2025-12-15",
  "verified": true,
  "helpful": 42,
  "images": [
    "https://images-na.ssl-images-amazon.com/images/I/abc123.jpg"
  ]
}

Aggregation Metadata

The actor also pushes a summary record (keyed with _metadata: true) containing:

{
  "_metadata": true,
  "productTitle": "Apple AirPods Pro (2nd Generation)",
  "platform": "amazon",
  "totalReviews": 47,
  "averageRating": 4.6,
  "ratingDistribution": {
    "1": 2,
    "2": 1,
    "3": 3,
    "4": 8,
    "5": 33
  }
}

Running Locally

# Install dependencies
pip install -r requirements.txt
playwright install chromium

# Set Apify token (or use local Actor context)
export APIFY_TOKEN=your_token_here

# Run
python -m src.main

License

MIT