# Analyze restaurant reviews: food, service, waiting time

**Use case:** 

Give the reviews of one or many restaurants and get, for each, what customers praise and criticize (food, service, waiting time, price, atmosphere, cleanliness), how serious the complaints are and which ones never got a reply. The sample uses six illustrative reviews; plug the dataset of any Google Maps reviews scraper with datasetId for real ones.

## Input

```json
{
  "reviews": [
    {
      "text": "The duck breast was perfect and the staff remembered our anniversary. We will be back.",
      "rating": 5,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    },
    {
      "text": "Good food but we waited 45 minutes for the main course and nobody told us why.",
      "rating": 3,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    },
    {
      "text": "Way too expensive for the portion size, and the table by the kitchen door was noisy.",
      "rating": 2,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    },
    {
      "text": "Lovely terrace, friendly waiter, desserts to die for.",
      "rating": 5,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    },
    {
      "text": "The fish smelled off and the restroom was dirty. Not coming back.",
      "rating": 1,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    },
    {
      "text": "Solid lunch menu at a fair price, quick service for a work lunch.",
      "rating": 4,
      "placeName": "Le Petit Comptoir (sample)",
      "category": "French restaurant"
    }
  ],
  "businessType": "restaurant",
  "maxReviews": 500,
  "maxReviewsPerPlace": 100,
  "includeReviewText": true
}
```

## Output

```json
{
  "placeName": {
    "label": "Business",
    "format": "string"
  },
  "reviewsAnalyzed": {
    "label": "Reviews",
    "format": "integer"
  },
  "averageRating": {
    "label": "Rating",
    "format": "number"
  },
  "negativeShare": {
    "label": "Negative share",
    "format": "number"
  },
  "unansweredComplaints": {
    "label": "Unanswered complaints",
    "format": "integer"
  },
  "summary": {
    "label": "Summary",
    "format": "string"
  }
}
```

## How it works

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

The Review Analyzer API — Sentiment, Topics & Complaints Actor is ready to run with the inputs already set for "Analyze restaurant reviews: food, service, waiting time". [Sign up or log in](https://console.apify.com/create-task-from-example/IZDl4VmhZ4Rf3GoVI) and you'll land in Apify Console with a copy of this exact task.

### 2. Start the "Analyze restaurant reviews: food, service, waiting time" task

The Review Analyzer API — Sentiment, Topics & Complaints Actor will start running based on the input automatically. You can also change all the input fields, like reviews, business type, and more, to match your specific use case.

### 3. Receive the "Analyze restaurant reviews: food, service, waiting time" 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 business, reviews, rating, 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 "Analyze restaurant reviews: food, service, waiting time" task via API](https://apify.com/sherwood/review-analyzer.md).

## About this Actor

This example demonstrates how to use [Review Analyzer API — Sentiment, Topics & Complaints](https://apify.com/sherwood/review-analyzer.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/sherwood/review-analyzer.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/sherwood/review-analyzer.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`).