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App Store & Google Play Reviews Scraper

Monitor app reviews across both Apple App Store and Google Play to catch feature requests, sentiment shifts, and quality issues before they snowball. Extract star ratings, review text, reviewer names, and version info for any app — then schedule it weekly to build a continuous feedback loop. Works with any app on either store using the bundle ID or numerical app ID.

Features

• Dual-store support — scrape reviews from both Google Play and Apple App Store in one actor
• Rich review data — extracts rating, title, full text, reviewer name, date, and app version
• Country targeting — filter reviews by country code to analyze regional sentiment
• Bulk extraction — fetch up to 5,000 reviews per run for deep analysis
• No browser needed — uses underlying store APIs for fast, reliable extraction
• Pay-per-result pricing — only pay for reviews extracted, with charge-limit safety built in

Input

Input Example

{
"appId":"com.whatsapp",
"store":"google",
"country":"us",
"maxReviews":200
}

Output

Each dataset item represents one user review. Key fields:

• appId (string) — the app identifier that was queried
• store (string) — google or apple
• id (string) — unique review identifier
• userName (string) — reviewer display name
• title (string) — review title (may be null on Google Play)
• text (string) — full review text
• score (number) — star rating (1-5)
• date (string) — review date
• version (string) — app version the review was written for

Output Example

{
"appId":"com.whatsapp",
"store":"google",
"id":"gp:AOqpTOH8x7...",
"userName":"Sarah K.",
"title":null,
"text":"Latest update broke voice messages. They cut off after 30 seconds. Please fix!",
"score":2,
"date":"2025-01-18T00:00:00.000Z",
"version":"2.25.1.8"
}

Pricing

Use Cases

• Product feedback analysis — mine reviews for feature requests, complaints, and praise patterns
• Sentiment monitoring — schedule weekly runs to track rating trends after app updates
• Competitive benchmarking — compare review sentiment across competing apps in your category
• QA regression detection — catch version-specific bugs reported by users after releases
• Market research — analyze user needs and pain points before entering a new app category
• AI-powered insights — feed review data into LLMs for automated sentiment classification and summarization

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