# Compare store wait times and cleanliness from reviews

**Use case:** 

Run a fictional two-location review sample to compare mentions of waiting time and cleanliness, with counts and small-sample warnings. Supply reviews you have permission to analyze for real results. This task does not scrape Google Maps. Check live Apify pricing before running.

## Input

```json
{
  "reviews": [
    {
      "location": "Harbor Cafe - North",
      "text": "Long wait at the counter, but the coffee was great.",
      "stars": 3
    },
    {
      "location": "Harbor Cafe - North",
      "text": "Waited 30 minutes for a simple order. Slow service.",
      "stars": 2
    },
    {
      "location": "Harbor Cafe - North",
      "text": "Friendly staff and clean tables.",
      "stars": 5
    },
    {
      "location": "Harbor Cafe - North",
      "text": "Wrong order again and the line was very slow.",
      "stars": 2
    },
    {
      "location": "Harbor Cafe - North",
      "text": "Nice place, a bit pricey.",
      "stars": 4
    },
    {
      "location": "Harbor Cafe - South",
      "text": "Quick service and lovely staff.",
      "stars": 5
    },
    {
      "location": "Harbor Cafe - South",
      "text": "Tables were dirty and the restroom smelled.",
      "stars": 2
    },
    {
      "location": "Harbor Cafe - South",
      "text": "Great coffee, fair price.",
      "stars": 5
    },
    {
      "location": "Harbor Cafe - South",
      "text": "Very noisy inside, hard to talk.",
      "stars": 3
    },
    {
      "location": "Harbor Cafe - South",
      "text": "Dirty floor but fast and polite.",
      "stars": 3
    }
  ],
  "topics": [
    "wait_time",
    "cleanliness"
  ],
  "customTopics": {
    "loyalty_program": [
      "loyalty card",
      "points",
      "stamp card"
    ]
  },
  "minReviewsPerLocation": 5,
  "minCellCount": 2,
  "minDifferencePoints": 5,
  "fdrAlpha": 0.05,
  "maxReviews": 50000,
  "maxLocations": 200
}
```

## Output

```json
{
  "location": {
    "label": "Location",
    "format": "string"
  },
  "topic": {
    "label": "Topic",
    "format": "string"
  },
  "reviewsInLocation": {
    "label": "Reviews",
    "format": "integer"
  },
  "mentionCount": {
    "label": "Mentions",
    "format": "integer"
  },
  "prevalencePct": {
    "label": "Prevalence %",
    "format": "number"
  },
  "peerPrevalencePct": {
    "label": "Peers %",
    "format": "number"
  },
  "differencePoints": {
    "label": "Diff (pts)",
    "format": "number"
  },
  "qValue": {
    "label": "q-value",
    "format": "number"
  },
  "direction": {
    "label": "Direction",
    "format": "string"
  },
  "status": {
    "label": "Status",
    "format": "string"
  },
  "sampleNote": {
    "label": "Sample note",
    "format": "string"
  }
}
```

## How it works

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

The Maps Reviews Issue Matrix by Location Actor is ready to run with the inputs already set for "Compare store wait times and cleanliness from reviews". [Sign up or log in](https://console.apify.com/create-task-from-example/Y6CS4aVcmYZ7nOG2f) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Compare store wait times and cleanliness from reviews" task

The Maps Reviews Issue Matrix by Location Actor will start running based on the input automatically. You can also change all the input fields, like inline review rows, built-in issue topics, and more, to match your specific use case.

### 3. Receive the "Compare store wait times and cleanliness from reviews" 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 location, topic, reviews, 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 "Compare store wait times and cleanliness from reviews" task via API](https://apify.com/practical_ai_lab_jp/pal-maps-reviews-issue-matrix.md).

## FAQ

### Does this task scrape Google Maps?

No. The task compares keyword-topic mentions in reviews you supply and are authorized to process. It never fetches Google Maps reviews.

### How much data is needed for reliable results?

The included fictional example has only five reviews per location for demonstration. For real comparisons, at least 100 reviews per location is a useful starting point.

### Can it identify negative sentiment automatically?

No. It counts issue-topic keyword mentions, not sentiment. Negation and sarcasm are not handled, so a mention is not necessarily a complaint.

## About this Actor

This example demonstrates how to use [Maps Reviews Issue Matrix by Location](https://apify.com/practical_ai_lab_jp/pal-maps-reviews-issue-matrix.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/practical_ai_lab_jp/pal-maps-reviews-issue-matrix.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/practical_ai_lab_jp/pal-maps-reviews-issue-matrix.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`).