Product Review Aggregator

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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

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)
exportAPIFY_TOKEN=your_token_here
# Run
python -m src.main

License

MIT