# App Store & Google Play Review Analyzer (`tuhin/app-review-analyzer`) Actor

Scrape App Store reviews and Google Play ratings, then analyze sentiment, rating distribution, top complaints and praises, and keyword themes.

- **URL**: https://apify.com/tuhin/app-review-analyzer.md
- **Developed by:** [Tuhin](https://apify.com/tuhin) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 66.7% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.90 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## App Store & Google Play Review Analyzer

**Scrape app reviews and instantly see what users love and hate.**

Give it App Store apps (by name, ID, or URL) and Google Play apps (by package ID), and get per-app *review analysis*: rating distribution, sentiment breakdown, **top complaints**, **top praises**, keyword themes, and whether the rating is improving or declining — plus every individual review.

> **Responsible use:** Uses only *public* endpoints — Apple's iTunes reviews RSS/lookup and Google Play's public app pages (JSON-LD). No login, no anti-bot bypass, no private data.

### Who it's for

App developers, product managers, ASO/marketing teams, and competitor researchers.

### Input

| Field | Type | Description |
|-------|------|-------------|
| `appStoreApps` | array | App names, IDs, or `apps.apple.com` URLs (e.g. `"Spotify"`, `"324684580"`). |
| `googlePlayApps` | array | Play package IDs or URLs (e.g. `"com.spotify.music"`). |
| `countries` | array | Store country codes, default `["us"]`. |
| `maxReviewsPerApp` | integer | App Store reviews per country (≤500). Default 200. |
| `includeAnalysis` | boolean | Compute sentiment/themes. Default true. |

#### Example

```json
{ "appStoreApps": ["Notion","Spotify"], "googlePlayApps": ["com.spotify.music"], "countries": ["us","gb"], "maxReviewsPerApp": 300 }
```

### Output

#### Per-app analysis (default dataset)

```json
{
  "appName": "Spotify: Music and Podcasts", "store": "app-store", "country": "us",
  "avgRating": 4.0, "storeAvgRating": 4.7, "storeRatingCount": 32000000,
  "reviewsAnalyzed": 300,
  "ratingDistribution": { "1": 40, "2": 15, "3": 20, "4": 35, "5": 190 },
  "sentimentBreakdown": { "positive": 153, "negative": 48, "neutral": 99 },
  "positivePct": 51, "negativePct": 16,
  "topComplaints": ["ads","premium","money","song","plays"],
  "topPraises": ["love","best","amazing","awesome"],
  "keywordThemes": [{ "word": "ads", "count": 61 }],
  "ratingTrend": "declining",
  "collectedAt": "2026-08-30T18:00:00Z"
}
```

#### Individual reviews (named dataset `reviews`)

```json
{ "store":"app-store","country":"us","appName":"Spotify","rating":2,"title":"Ads out of control","text":"...","author":"...","version":"8.9","updated":"2026-08-28T...","sentiment":"negative" }
```

Run summary in key-value store `OUTPUT`.

### How the analysis works (transparent)

- **Sentiment:** lexicon-based positive/negative scoring per review (no black-box model).
- **Top complaints / praises:** *distinctive* terms — words that appear disproportionately in negative vs positive reviews (not just frequent words), so you get real differentiators.
- **Rating trend:** average rating of the most-recent half vs the older half → improving / stable / declining.

### Notes / limitations

- **App Store:** full review text + analysis (Apple RSS returns up to ~500 most-recent reviews per country).
- **Google Play (v1):** app-level rating, rating count, developer, category and description from the public app page. Per-review *text* on Google Play requires a fragile internal endpoint and is out of scope for v1 — the analysis fields are App Store based.
- Sentiment/themes are heuristic signals for triage, not a trained model.

# Actor input Schema

## `appStoreApps` (type: `array`):

App Store app IDs, apps.apple.com URLs, or app names to search, e.g. "324684580", "https://apps.apple.com/us/app/spotify/id324684580", or "Spotify".

## `googlePlayApps` (type: `array`):

Google Play package IDs or URLs, e.g. "com.spotify.music". App-level rating/metadata is collected (see notes on review depth).

## `countries` (type: `array`):

2-letter store country codes for reviews/metadata, e.g. "us", "gb", "de".

## `maxReviewsPerApp` (type: `integer`):

App Store RSS returns up to ~500 most-recent reviews per country.

## `includeAnalysis` (type: `boolean`):

Compute sentiment, rating distribution, top complaints/praises and keyword themes per app.

## `proxyConfiguration` (type: `object`):

Optional. These public endpoints work fine without a proxy.

## Actor input object example

```json
{
  "appStoreApps": [
    "324684580",
    "Notion"
  ],
  "googlePlayApps": [
    "com.spotify.music"
  ],
  "countries": [
    "us"
  ],
  "maxReviewsPerApp": 200,
  "includeAnalysis": true,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `appAnalysis` (type: `string`):

One analysis record per app/country. Individual reviews are in the named dataset 'reviews'; run summary in key-value store OUTPUT.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "appStoreApps": [
        "Spotify"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tuhin/app-review-analyzer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "appStoreApps": ["Spotify"] }

# Run the Actor and wait for it to finish
run = client.actor("tuhin/app-review-analyzer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "appStoreApps": [
    "Spotify"
  ]
}' |
apify call tuhin/app-review-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tuhin/app-review-analyzer"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/0LtmgbYqh4UMlWWsE/builds/7oa5EBRZLQWckneRo/openapi.json