# Google Play Credit App Reviews Analysis (`bitlabai/google-play-credit-app-reviews-analysis`) Actor

This Actor collects negative Google Play reviews for lending apps, extracts key loan signals (disbursed amount, repayment, term, and fee rate) with an LLM, labels extraction quality, and outputs structured records plus summary statistics for risk and product analysis.

- **URL**: https://apify.com/bitlabai/google-play-credit-app-reviews-analysis.md
- **Developed by:** [Bitlab AI](https://apify.com/bitlabai) (community)
- **Categories:** AI, Automation, Agents
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $15.00 / 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/platform/actors/running/actors-in-store#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

### What does google-play-credit-app-reviews-analysis do?

This Actor analyzes negative reviews (rating 1-2) from a target Google Play credit app page and extracts core lending signals from user complaints. It calls the Apify Store Actor [webdatalabs/google-play-reviews-scraper](https://apify.com/webdatalabs/google-play-reviews-scraper) to collect reviews, then uses [OpenRouter](https://openrouter.ai) to call a large language model (fixed model: `qwen/qwen3.7-plus`) to infer:

- Loan disbursed amount (actual arrival amount)
- Repayment amount
- Repayment term
- Fee rate = (repayment amount - arrival amount) / repayment amount

It outputs per-review structured records and one summary record with amount range, amount median, repayment term distribution, and fee rate median.

### Why use this Actor?

- Understand user-reported loan economics from complaint-heavy reviews.
- Estimate real user burden from review text, not only app metadata.
- Identify suspicious repayment conditions and abnormal fee rates quickly.
- Build data feeds for risk dashboards, compliance checks, and product benchmarking.

### How to use this Actor

1. Open the Actor input form.
2. Set Google Play app URL in `startUrls`.
3. Set scraping settings (`maxReviews`, `sortBy`, `proxyConfiguration`).
4. Optionally adjust `confidenceThreshold` and outlier-statistics behavior.
5. Run the Actor.
6. Read dataset output for review-level rows and one `recordType = summary` row.

### Environment Setup

Before running this Actor, configure these environment variables:

**Local testing (PowerShell):**

```powershell
$env:APIFY_TOKEN = "your-apify-api-token"
$env:OPENROUTER_API_KEY = "your-openrouter-api-key"
apify run
```

**Permanent configuration (Windows):**

```powershell
[Environment]::SetEnvironmentVariable("APIFY_TOKEN", "your-apify-api-token", "User")
[Environment]::SetEnvironmentVariable("OPENROUTER_API_KEY", "your-openrouter-api-key", "User")
```

**On Apify Platform:**
Add `OPENROUTER_API_KEY` to the Actor's Secrets in the Apify Console. `APIFY_TOKEN` is automatically provided by the platform.

Get your tokens at:

- [Apify Console](https://console.apify.com) → Account → API tokens
- [OpenRouter Dashboard](https://openrouter.ai) → Dashboard → API keys

### Input

This Actor accepts the same scraping controls as the upstream scraper:

- `startUrls` (required): Google Play app URLs.
- `maxReviews`: max number of reviews requested from upstream actor.
- `sortBy`: sort mode passed through to upstream actor.
- `proxyConfiguration`: proxy config passed through to upstream actor.

Analysis controls:

- `confidenceThreshold` (default 0.6): review extraction confidence threshold.
- `includeOutliersInStats` (default false): whether fee-rate outliers (<10% or >80%) are included in core fee median.

Internally forced values when calling upstream actor:

- `rating = NEGATIVE`
- `language = en`

### Output

Output is pushed to default dataset.

- Multiple rows with `recordType = review`.
- Exactly one row with `recordType = summary`.

Simplified example:

```json
[
    {
        "recordType": "review",
        "appId": "com.example.loan",
        "reviewRating": 1,
        "arrivalAmount": 7000,
        "arrivalCurrency": "KES",
        "repaymentAmount": 9100,
        "repaymentTermNormalized": "7",
        "feeRate": 0.2308,
        "feeRateOutlier": false,
        "extractionConfidence": 0.82,
        "extractionQuality": "HIGH"
    },
    {
        "recordType": "summary",
        "appId": "com.example.loan",
        "processedReviewCount": 78,
        "qualityStats": {
            "highQualityCount": 28,
            "mediumQualityCount": 34,
            "lowQualityCount": 16
        },
        "arrivalAmountByCurrency": {
            "KES": { "min": 1500, "max": 12000, "median": 6000, "count": 74 }
        },
        "repaymentTermDistribution": { "7": 44, "15": 17, "30": 9, "X*3": 8 },
        "feeRateMedian": 0.26
    }
]
```

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

### Data table

| Field | Description |
| --- | --- |
| `arrivalAmount` | Disbursed amount detected from review text |
| `repaymentAmount` | Repayment amount detected from review text |
| `repaymentTermNormalized` | Normalized term (e.g. 7, 15, 30, 90, 7*N, X*N) |
| `feeRate` | Computed or extracted fee rate ratio |
| `feeRateOutlier` | True if fee rate < 0.1 or > 0.8 |
| `extractionConfidence` | LLM extraction confidence score |

### Pricing / Cost estimation

How much does it cost to analyze one app?

- Upstream scraping cost depends on review volume and proxy usage.
- LLM cost scales with number of negative reviews and review text length.
- Start with smaller `maxReviews` (e.g. 100) to validate data quality first.

### Tips and advanced options

- Keep `confidenceThreshold` around 0.3 to retain more records, then filter by `extractionQuality` (`HIGH`/`MEDIUM`/`LOW`) in downstream analysis.
- Increase threshold if your workflow is strict and low-noise.
- Keep `includeOutliersInStats = false` for stable central tendency metrics.
- If many records are filtered out, check language quality and app region conventions.

### FAQ, disclaimers, and support

- Review text is user-generated and may be incomplete, sarcastic, or contradictory.
- Multi-language and slang can reduce extraction accuracy.
- Currency detection follows text clues and may return `UNKNOWN`.
- This Actor provides analytical inference, not legal or financial advice.

If you want a custom extraction schema or country-specific term/currency handling, open an issue in your repository and extend the extraction prompt and post-processing rules.

# Actor input Schema

## `startUrls` (type: `array`):

Google Play app detail URLs to analyze.

## `maxReviews` (type: `integer`):

Maximum reviews requested from upstream scraper actor.

## `sortBy` (type: `string`):

Sort mode passed through to upstream scraper actor.

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

Proxy settings passed through to upstream scraper actor.

## `confidenceThreshold` (type: `number`):

Quality threshold. All records are output with HIGH/MEDIUM/LOW tags. Confidence >= 0.8 = HIGH; >= threshold = MEDIUM; < threshold = LOW.

## `includeOutliersInStats` (type: `boolean`):

If true, fee-rate outliers (<10% or >80%) are included in fee median stats.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://play.google.com/store/apps/details?id=com.mojang.minecraftpe"
    }
  ],
  "maxReviews": 100,
  "sortBy": "NEWEST",
  "proxyConfiguration": {
    "useApifyProxy": true
  },
  "confidenceThreshold": 0.3,
  "includeOutliersInStats": false
}
```

# 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 = {
    "startUrls": [
        {
            "url": "https://play.google.com/store/apps/details?id=com.mojang.minecraftpe"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("bitlabai/google-play-credit-app-reviews-analysis").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 = { "startUrls": [{ "url": "https://play.google.com/store/apps/details?id=com.mojang.minecraftpe" }] }

# Run the Actor and wait for it to finish
run = client.actor("bitlabai/google-play-credit-app-reviews-analysis").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 '{
  "startUrls": [
    {
      "url": "https://play.google.com/store/apps/details?id=com.mojang.minecraftpe"
    }
  ]
}' |
apify call bitlabai/google-play-credit-app-reviews-analysis --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=bitlabai/google-play-credit-app-reviews-analysis",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/MXLfAblUXF1oKpcIc/builds/7Tnq6aJjJdRY7wT25/openapi.json