Google Play Reviews Scraper

Scrape Google Play Store app reviews at scale — star rating, review text, author, date and app version. No API key, no anti-bot wall.

Export every public review for any Android app on the Google Play Store by package name. Built for ASO (app store optimization), app developers, product teams and sentiment analysis — point it at your app (or a competitor's) and get a clean, structured review dataset ready for CSV/JSON/Excel or an AI pipeline.

Reads directly from Google Play's public batchexecute endpoint — no API key required, no login, no proxy needed for most volumes.

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What you get

For every app you give it, the actor returns one row per review:

Field	Description
appId	Play Store package name (e.g. com.spotify.music)
reviewId	Unique review ID
author	Reviewer display name
avatarUrl	Reviewer profile photo URL
score	Star rating, 1–5
text	Full review text
date	Review date (YYYY-MM-DD)
timestamp	Review time (ISO 8601)
thumbsUp	"Helpful" count
appVersion	App version the review was left on
criteria	Structured app-quality tags Google attaches to some reviews — [{aspect, rating}], e.g. ads_frequency, battery_heat, ads_presence, stability
replyText	Developer reply text, if any
replyDate	Developer reply date, if any
url	App's Play Store URL

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Input

Field	Type	Default	Description
appIds	array (required)	—	Play Store package names, e.g. com.whatsapp. Find it in the Play URL after ?id=.
country	string	us	Two-letter storefront code (us, gb, de…).
language	string	en	Two-letter review language code.
sort	string	newest	newest, rating, or helpfulness.
maxReviewsPerApp	integer	200	Cap per app (1–100000).

Example input

{
  "appIds": ["com.spotify.music"],
  "country": "us",
  "language": "en",
  "sort": "newest",
  "maxReviewsPerApp": 500
}

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Example run output

One dataset item per review:

{
  "appId": "com.spotify.music",
  "reviewId": "gp:AOqpTOG...",
  "author": "Ben Alvarez",
  "score": 1,
  "text": "App constantly crashes since the last update.",
  "date": "2026-06-18",
  "timestamp": "2026-06-18T14:22:07.000Z",
  "thumbsUp": 42,
  "appVersion": "9.1.58.1567",
  "criteria": [
    { "aspect": "ads_frequency", "rating": 2 },
    { "aspect": "battery_heat", "rating": 2 }
  ],
  "replyText": "",
  "replyDate": "",
  "url": "https://play.google.com/store/apps/details?id=com.spotify.music"
}

Export to CSV / Excel / JSON from the run's Dataset tab or via the API.

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How this compares

The most-installed Play-reviews actors on the Store fall into two camps, and each gives up something this one keeps:

• The user-base leader scrapes one app at a time with a thin ~11-field schema — no developer replies and no per-aspect quality criteria. This actor takes a list of apps (appIds[]) in a single run and returns both.
• The one rich competitor that matches the field depth has a notably lower Store rating.

So the wedge is honest and concrete: multi-app batch in one run + developer replies + the structured criteria quality tags + reviewer avatarUrl — the leader has none of the last three, and is single-app on top of that. criteria (Google's own ad-frequency / battery / stability flags per review) is the single most useful signal for ASO and is missing from almost every alternative.

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Use cases

• ASO / app store optimization — track review sentiment, ratings trend and version-by-version feedback.
• Competitor research — pull a rival app's reviews to find feature gaps and complaint themes.
• Product & QA — surface crash/bug reports tied to specific appVersion values.
• Sentiment analysis / AI — feed clean review text + scores into an LLM or BI pipeline.

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Pricing

Pay per result (per review). High-volume by design and priced to stay cheap at scale, so pulling tens of thousands of reviews stays affordable.

Notes

No API key needed. For very large pulls, enable Apify Proxy in the run options. One bad/missing app never kills the run — it logs a warning and moves on.