# App Store Review Intelligence (`knotted_tussock/app-store-review-intelligence`) Actor

Collect public Apple App Store reviews and generate app-level sentiment, issue, and feature-request signals for product and competitor research.

- **URL**: https://apify.com/knotted\_tussock/app-store-review-intelligence.md
- **Developed by:** [Ralph T](https://apify.com/knotted_tussock) (community)
- **Categories:** AI, Developer tools, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## 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 Review Intelligence

Collect public Apple App Store reviews and turn them into lightweight product-intelligence signals: negative/neutral/positive rating classes, keyword-group tags, app metadata, and an app-level summary.

### What it does

- Accepts Apple numeric app IDs or App Store URLs.
- Reads public Apple review RSS pages for a selected country storefront.
- Emits one dataset row per review with rating, title, text, version, author name, URL, and matched keyword groups.
- Writes a `SUMMARY` key-value-store record with per-app rating distribution, signal counts, latest review timestamp, and total review counts.

### Good use cases

- Product teams tracking recent App Store complaints and feature requests.
- Agencies comparing competitors before app-store optimization or retention work.
- Founders validating whether users complain about pricing, crashes, missing features, or onboarding.
- Review-intelligence pipelines that need clean rows without credentials or proxies.

### Input example

```json
{
  "appIds": ["284882215"],
  "country": "us",
  "maxReviewsPerApp": 100,
  "pagesPerApp": 2,
  "keywordGroups": {
    "pricing": ["price", "subscription", "expensive", "free", "pay"],
    "bugs": ["bug", "crash", "broken", "freeze", "error"],
    "features": ["feature", "wish", "please add", "missing", "need"]
  }
}
```

You can also pass `appUrls`, for example:

```json
{
  "appUrls": ["https://apps.apple.com/us/app/facebook/id284882215"],
  "country": "us",
  "maxReviewsPerApp": 50
}
```

### Output fields

Dataset rows include:

- `appId`, `appName`, `developer`, `country`
- `reviewId`, `rating`, `ratingClass`, `version`
- `title`, `content`, `author`, `updatedAt`
- `matchedGroups`, `matchedKeywords`
- `reviewUrl`, `appUrl`, `collectedAt`

The `SUMMARY` record includes per-app review counts, rating distributions, signal counts, and run parameters.

### Notes and limits

- Uses public Apple iTunes/App Store RSS and lookup endpoints only; no login, proxies, or private APIs are required.
- Apple RSS typically returns up to 50 reviews per page. Use `pagesPerApp` and `maxReviewsPerApp` to control cost and runtime.
- Reviews are storefront-specific; set `country` to the market you care about.
- Keyword matching is simple substring tagging intended for fast triage, not a substitute for human review or a full NLP sentiment model.

# Actor input Schema

## `appIds` (type: `array`):

Apple numeric app IDs, e.g. 284882215 for Facebook. Use either appIds or appUrls.

## `appUrls` (type: `array`):

Apple App Store URLs. The Actor extracts the numeric id from each URL.

## `country` (type: `string`):

Two-letter App Store country code used for reviews and app metadata.

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

Caps reviews emitted for each app.

## `pagesPerApp` (type: `integer`):

Apple RSS returns up to 50 reviews per page. Keep low for quick checks.

## `minRating` (type: `integer`):

Only include reviews with this rating or higher.

## `maxRating` (type: `integer`):

Only include reviews with this rating or lower.

## `keywordGroups` (type: `object`):

Named keyword lists to tag reviews, e.g. bugs/pricing/features.

## Actor input object example

```json
{
  "appIds": [
    "284882215"
  ],
  "country": "us",
  "maxReviewsPerApp": 200,
  "pagesPerApp": 2,
  "minRating": 1,
  "maxRating": 5,
  "keywordGroups": {
    "pricing": [
      "price",
      "subscription",
      "expensive",
      "free",
      "pay"
    ],
    "bugs": [
      "bug",
      "crash",
      "broken",
      "freeze",
      "error"
    ],
    "features": [
      "feature",
      "wish",
      "please add",
      "missing",
      "need"
    ]
  }
}
```

# Actor output Schema

## `reviews` (type: `string`):

Reviews tagged with rating class, matched keyword groups, and app metadata.

## `summary` (type: `string`):

Summary JSON with per-app rating distribution and issue/feature/pricing keyword counts.

# 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 = {
    "appIds": [
        "284882215"
    ],
    "country": "us"
};

// Run the Actor and wait for it to finish
const run = await client.actor("knotted_tussock/app-store-review-intelligence").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 = {
    "appIds": ["284882215"],
    "country": "us",
}

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

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

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

```

## CLI example

```bash
echo '{
  "appIds": [
    "284882215"
  ],
  "country": "us"
}' |
apify call knotted_tussock/app-store-review-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=knotted_tussock/app-store-review-intelligence",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/YivUKGEoGWp4GbvYL/builds/DIFkfjL3iqd0tbfS8/openapi.json