# What users hate: 1-2 star analysis of language apps

**Use case:** 

Gain deep insights into what users dislike about popular language apps. This task analyzes 1-2 star reviews for Duolingo and Babbel across the US, UK, Canada, and Australia. Discover key complaints and recurring issues such as crashes, pricing, and ads, enabling you to target user concerns effectively. Get a detailed report with actionable insights on app performance.

## Input

```json
{
  "apps": [
    "https://apps.apple.com/us/app/duolingo/id570060128",
    "https://apps.apple.com/us/app/babbel/id829587759"
  ],
  "countries": [
    "us",
    "gb",
    "ca",
    "au"
  ],
  "output": "insights",
  "sort": "mostRecent",
  "maxRating": 2,
  "onlyNewReviews": false
}
```

## Output

```json
{
  "appName": {
    "label": "App name"
  },
  "developer": {
    "label": "Developer"
  },
  "reviewsAnalysed": {
    "label": "Reviews analysed"
  },
  "averageRating": {
    "label": "Average rating"
  },
  "storeRating": {
    "label": "Store rating"
  },
  "negativeShare": {
    "label": "Negative share"
  },
  "ratingTrend": {
    "label": "Rating trend"
  },
  "topics": {
    "label": "Topics"
  },
  "topComplaints": {
    "label": "Top complaints"
  },
  "topPraise": {
    "label": "Top praise"
  },
  "byVersion": {
    "label": "By version"
  },
  "appUrl": {
    "label": "App",
    "format": "link"
  }
}
```

## How it works

### 1. Sign up on Apify or log in

The App Store Review Insights: Reviews Scraper + Analysis Actor is ready to run with the inputs already set for "What users hate: 1-2 star analysis of language apps". [Sign up or log in](https://console.apify.com/create-task-from-example/vLZUL7BaK9aOCzULa) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "What users hate: 1-2 star analysis of language apps" task

The App Store Review Insights: Reviews Scraper + Analysis Actor will start running based on the input automatically. You can also change all the input fields, like apps, countries, and more, to match your specific use case.

### 3. Receive the "What users hate: 1-2 star analysis of language apps" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as app name, developer, reviews analysed, and more.

### 4. Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also [run the "What users hate: 1-2 star analysis of language apps" task via API](https://apify.com/clean_feeds/app-store-review-insights.md).

## About this Actor

This example demonstrates how to use [App Store Review Insights: Reviews Scraper + Analysis](https://apify.com/clean_feeds/app-store-review-insights.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/clean_feeds/app-store-review-insights.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/clean_feeds/app-store-review-insights.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).