⭐ Google Play Reviews Scraper + AI Sentiment Analysis

What does Google Play Reviews Scraper do?

Scrape user reviews from Google Play (Android) at scale and get AI-powered sentiment analysis, topic extraction, one-line summaries, and bug-vs-feature classification on every single review — not just raw text.

Most review scrapers dump unstructured text on you and leave the hardest part — extracting insights and categorizing complaints — entirely to manual labor. This Actor extracts public reviews directly from the Google Play Store and automatically enriches every item with built-in or LLM-driven intelligence. You go straight from thousands of raw reviews to structured, actionable product feedback.

Powered by the Apify platform, you can run this scraper on demand, schedule automated recurring runs, or integrate it directly into your data pipelines via the Apify REST API and webhooks — no proxy required.

Why use Google Play Reviews Scraper?

• Zero-setup AI enrichment: Every review is scored for sentiment (positive, neutral, negative) and categorized by type (bug, feature_request, praise, complaint, question, other) out of the box.
• Deep pagination & historical coverage: Google Play allows deep pagination so you can pull hundreds or thousands of reviews across any app version.
• Global reach: Target any country store locale (us, gb, br, de, fr, in, etc.) and language (en, pt, es, de, etc.) to track international user feedback.
• Bring your own LLM (optional): Connect any OpenAI-compatible API (OpenAI, Groq, Together AI, Ollama, vLLM) for custom topic extraction and executive summaries, or use the fast built-in analyzer with zero configuration.
• Automated reputation & bug triage: Schedule runs to pipe fresh 1-star reviews or newly detected bug reports directly into Slack, Discord, email, or your ticketing system.

How to use Google Play Reviews Scraper

1. Find your target app: Obtain the Google Play package name from the store URL (e.g. for https://play.google.com/store/apps/details?id=com.whatsapp, the package name is com.whatsapp).
2. Configure input parameters:
   • Set App ID or package name (e.g. com.whatsapp) or paste the full Google Play URL.
   • Choose your target Country code (e.g. us, br, de) and Language (e.g. en, pt).
   • Specify Max reviews to collect (e.g. 100 or 5000).
   • Keep Enable AI analysis checked.
3. Click Start: The Actor will extract and enrich the reviews.
4. Download your data: Export the resulting dataset in JSON, CSV, Excel, XML, or HTML format.

Input

Configure your scraping job using the following fields in the Input tab:

Field	Type	Description	Default / Example
store	String	Target store (googleplay is recommended default; appstore uses resilient RSS scraping).	googleplay
appId	String	Google Play package name (e.g. com.whatsapp) or Apple App Store numeric ID (e.g. 310633997).	com.whatsapp
url	String	Full store URL (alternative to appId).	https://play.google.com/store/apps/details?id=com.whatsapp
country	String	Two-letter country code of the store locale.	us
language	String	Two-letter language code for the reviews.	en
maxReviews	Integer	Maximum number of reviews to scrape.	100
enrich	Boolean	Enable automatic AI sentiment and classification.	true
llmBaseUrl	String	(Optional) Custom OpenAI-compatible endpoint (e.g. https://api.openai.com/v1).	""
llmModel	String	(Optional) Model name for the custom endpoint (e.g. gpt-4o-mini).	""
llmApiKey	String	(Optional) Bearer token for your LLM provider.	""

Input JSON example

{
  "store": "googleplay",
  "appId": "com.whatsapp",
  "country": "us",
  "language": "en",
  "maxReviews": 100,
  "enrich": true
}

Output

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

Below is a real output record extracted from Google Play with built-in AI enrichment:

{
  "store": "googleplay",
  "appId": "com.whatsapp",
  "country": "us",
  "reviewId": "5d16ffcf-fa31-419f-b989-d419ad24d35e",
  "title": null,
  "text": "new whatsapp some char cantt be pinned why so so this issue fix please as well as whatsapp community members limit 2000 increase it 5000",
  "rating": 5,
  "version": "2.26.32.83",
  "author": "RDa_Fanai",
  "thumbsUp": 0,
  "date": "2026-09-05T09:08:55",
  "ai": {
    "sentiment": "positive",
    "topics": [],
    "summary": null,
    "type": "bug",
    "method": "keyword"
  }
}

When an external LLM endpoint is provided, the ai block is populated with topics and a generated summary:

{
  "store": "googleplay",
  "appId": "com.whatsapp",
  "country": "us",
  "reviewId": "5d16ffcf-fa31-419f-b989-d419ad24d35e",
  "title": null,
  "text": "new whatsapp some char cantt be pinned why so so this issue fix please as well as whatsapp community members limit 2000 increase it 5000",
  "rating": 5,
  "version": "2.26.32.83",
  "author": "RDa_Fanai",
  "thumbsUp": 0,
  "date": "2026-09-05T09:08:55",
  "ai": {
    "sentiment": "positive",
    "topics": ["pinned characters", "community limit"],
    "summary": "User requests fixing character pinning and expanding community member limits.",
    "type": "feature_request",
    "method": "llm"
  }
}

Data table

Field	Type	Description
store	String	Always "googleplay".
appId	String	Google Play package name (e.g. com.whatsapp).
country	String	Country code of the store locale where the review was posted.
reviewId	String	Unique identifier of the review in Google Play.
title	String / Null	Review title (null on Google Play as Android reviews are text-only).
text	String	Full review text written by the user.
rating	Integer	Rating score given by the user (1 to 5 stars).
version	String	App version installed by the user when posting the review.
author	String	Public display name of the reviewer.
thumbsUp	Integer	Number of helpful / thumbs-up votes received from other users.
date	String	ISO 8601 timestamp when the review was posted.
ai.sentiment	String	Sentiment evaluation: positive, neutral, or negative.
ai.type	String	Classification: bug, feature_request, praise, complaint, question, or other.
ai.topics	Array	Key topics discussed (populated when LLM is configured).
ai.summary	String / Null	Concise summary sentence (populated when LLM is configured).
ai.method	String	Analysis engine: keyword (built-in) or llm (custom endpoint).

Pricing / Cost estimation

How much does it cost to scrape Google Play reviews?

• Pay-per-event pricing: $0.15 per 1,000 reviews scraped, plus a minimal platform actor-start event.
• AI enrichment included: Built-in sentiment analysis and bug-vs-feature categorization are included at no extra charge.
• No proxy fees: Direct connection — no datacenter or residential proxy surcharges.
• Free tier usage: Apify provides free monthly platform credits, allowing you to run test scrapes without upfront costs.

Tips & Advanced options

Automated daily review monitoring & alerting

Connect this Actor to the Apify Schedules feature (e.g. cron 0 8 * * *) and pipe the output to Slack, Microsoft Teams, Discord, Google Sheets, or Zapier/Make. Filter by rating <= 2 or ai.type == "bug" to build an automated real-time alert system for critical production bugs reported by users.

Competitor app intelligence

Run the scraper periodically across competitor package names to monitor their rating trends after major updates, identify common user pain points, and uncover feature opportunities.

Filtering by country & language

User expectations and complaints often vary drastically by region. Scrape specific store locales by setting country (us, br, de, in, jp, etc.) and language to localize your product roadmap.

FAQ, disclaimers, and support

How does the Actor handle Apple App Store (iOS) reviews?

Apple's public customer reviews RSS feed (itunes.apple.com/.../rss/customerreviews/...) is available but unstable: it is not discontinued, yet its availability varies by storefront and by page number in a non-deterministic way. The same feed returns a full page of 50 reviews for some combinations of app, country and page, and an HTTP 200 envelope with no entry key for others.

We measured this across 4 apps × 3 storefronts × 10 pages. Two findings drive the actor's design:

• Page 1 returned no reviews in all 12 combinations tested. A scraper that stops at the first empty page therefore collects nothing, even when reviews are available.
• Reviews were reachable in 11 of the 12 combinations once every page was scanned. Which page holds the data is unpredictable — data appeared on pages 2 through 10 depending on the combination.

The Actor is built around those measurements: in store=appstore mode it scans pages 1-10 instead of stopping at empty pages, and if the requested storefront yields nothing it automatically retries against the us and gb storefronts before giving up. When a fallback storefront supplies the data, the run logs which storefront was actually used, and each item carries that storefront in its country field.

If every page and every fallback storefront comes back empty, the Actor does not fail and does not finish silently: it emits an explicit warning and a run status message stating that Apple's public feed was unavailable for that combination, and recommending store=googleplay. Google Play remains the default because it is consistently available and supports deep pagination for large pulls.

Is scraping Google Play reviews legal?

This Actor extracts only publicly accessible reviews published on the Google Play Store. It does not collect private personal data beyond the public reviewer username displayed by Google Play. You are responsible for complying with Google's Terms of Service and applicable local regulations (such as GDPR).

Can I run this Actor via API?

Yes. Every Actor on Apify can be called programmatically via REST API, Python client (apify-client), JavaScript/TypeScript SDK, or webhooks.

Support and feedback

If you encounter any issues, require custom scrapers, or have feature requests, please report them via the Issues tab on the Actor page.