# Review Analyzer API — Sentiment, Topics & Complaints (`sherwood/review-analyzer`) Actor

Analyze customer reviews in bulk: sentiment, topics praised or criticized, complaint severity and unanswered complaints, with a summary per business. Works on any Google Maps, Trustpilot or Tripadvisor reviews dataset. Pay per review.

- **URL**: https://apify.com/sherwood/review-analyzer.md
- **Developed by:** [Sherwood](https://apify.com/sherwood) (community)
- **Categories:** AI, Marketing, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 review analyzeds

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

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 examples already wired to this Actor's own input schema, see the [API](#api) section below.

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`).

# README

## Review Analyzer API — sentiment, topics and unanswered complaints, pay per review

Give it the reviews of one business or of a thousand, as a list or as the dataset of any reviews scraper (Google Maps, Trustpilot, Tripadvisor...), and get for each business a summary built from every review: what customers praise, what they criticize, how serious the complaints are, which complaints never got a reply, and what changed over the last 90 days. **$0.001 per review analyzed**, reviews without text are free.

Built for automation and AI agents: nothing is required, the default input analyzes five sample reviews on its own, field names of the common scrapers are detected automatically, and the output is one plain dataset item per business.

### What you get

One item per business, with its analyzed reviews inside:

```json
{
  "placeName": "Hotel Example",
  "businessType": "hotel",
  "reviewsAnalyzed": 15,
  "averageRating": 2.3,
  "positive": 2, "mixed": 0, "negative": 13,
  "negativeShare": 0.87,
  "recentNegativeShare": 0.9,
  "olderNegativeShare": 0.6,
  "ownerReplyRate": 0.0,
  "unansweredComplaints": 12,
  "strengths": [{"theme": "location", "mentions": 1}],
  "weaknesses": [{"theme": "staff", "mentions": 10}, {"theme": "cleanliness", "mentions": 7}, {"theme": "room", "mentions": 7}],
  "alerts": [{"date": "2026-10-03", "rating": 1, "severity": 3.0, "excerpt": "The dirtiest hotel...", "url": "https://..."}],
  "summary": "Strengths: location (1). Weaknesses: staff (10), cleanliness (7), room (7). 13 negative and 0 mixed out of 15 reviews. 12 complaints without a reply. 5 serious alerts.",
  "reviews": [
    {
      "reviewDate": "2026-10-03", "reviewRating": 1, "sentiment": "negative", "severityLabel": "serious",
      "criticized": ["cleanliness", "staff"], "praised": [], "ownerReplied": false, "needsReply": 0.97,
      "recommendation": "discourages", "reviewUrl": "https://..."
    }
  ]
}
```

The **Reviews** view of the dataset (or `items?unwind=reviews` in the API) gives one row per review, ready for a spreadsheet.

### What each review is checked for

- **Sentiment**: positive, mixed or negative. The rating alone is not enough: a 4-star review with a complaint is mixed.
- **Themes**, each as not mentioned, praised or criticized, so one review can praise the food and criticize the waiting time. The themes depend on the business type, detected from its category:
  - restaurant: food, service, waiting time, price, atmosphere, cleanliness;
  - hotel: room, cleanliness, staff, location, noise, breakfast, value;
  - health: reception, waiting time, listening, care quality, fees, appointments;
  - trades: punctuality, work quality, price and quote, honesty, communication;
  - beauty, professional services, retail and a generic set.
    Or give your own with `themes` (`"parking: parking space and access"`).
- **Severity** of the main complaint: none, annoyance, problem, or serious (hygiene, safety, health, scam, discrimination). Serious ones come back as `alerts`.
- **Needs a reply**: a complaint, an incident or a question. Combined with the owner's reply, it gives `unansweredComplaints`.
- **Recommendation**: recommends, neutral or discourages.

Reviewer names and photos are never read or copied.

### Use cases

- Reputation management agencies: find the businesses with unanswered complaints in the last weeks, with the exact reviews to show.
- Multi-site brands and franchises: compare locations on the same themes every month.
- Market research: what customers praise and criticize in a category and a city.
- Buying a business: check the trend and the serious complaints before the deal.

### Chain it after a reviews scraper

Give `datasetId` and the Actor reads the reviews from its items. Text, rating, date, owner reply and business are detected, and were tested on the real output of the most used scrapers:

| Source | Scraper | Notes |
|---|---|---|
| Google Maps | `compass/Google-Maps-Reviews-Scraper` | one item per review |
| Google Maps | `compass/crawler-google-places` with `maxReviews` | reviews nested in each place are unfolded |
| Tripadvisor | `maxcopell/tripadvisor-reviews` | the review title is kept in front of the text |
| Trustpilot | `memo23/trustpilot-scraper-ppe` | the review title is kept in front of the text |
| Booking.com | `voyager/booking-reviews-scraper` | liked and disliked parts read together; scores out of 10 rescaled |
| Yelp | `tri_angle/yelp-review-scraper` | |

For another source, set `textField`, `ratingField` and the other field options if detection fails.

```json
{ "datasetId": "YOUR_REVIEWS_DATASET_ID", "maxReviewsPerPlace": 50 }
```

To run it automatically after a reviews scraper, add it on that scraper's **Integrations** tab with the input `{ "datasetId": "{{resource.defaultDatasetId}}" }`.

### Input

| Field | Type | Default | Notes |
|---|---|---|---|
| `reviews` | array | five sample reviews | Strings, or objects with `text` and optionally `rating`, `date`, `placeName`, `category`, `reviewUrl`. |
| `datasetId` | string | — | Read the reviews from an Apify dataset instead. |
| `businessType` | select | `auto` | Picks the default themes. |
| `themes` | array | — | Your own themes, replacing the defaults. |
| `maxReviews` | integer | `500` | Reviews with a text analyzed per run. |
| `maxReviewsPerPlace` | integer | `100` | Most recent reviews kept per business. |
| `includeReviewText` | boolean | `true` | Copy the first 1,000 characters of each review into its result. |

### Pricing

- **Review analyzed** — $0.001 per review with a text (= $1 per 1,000).
- Reviews without text, and reviews that could not be analyzed: free.

Set a maximum cost per run in Apify and the Actor stops cleanly when it is reached; the remaining reviews are reported, not analyzed.

### Via API, n8n, Make or MCP

`POST https://api.apify.com/v2/actors/sherwood~review-analyzer/run-sync-get-dataset-items` with the JSON input above, or add it as a tool through the Apify MCP server.

### Limits

- The analysis is made by an AI model (Jev, by TypeSafe AI) that classifies each review; check the reviews behind an alert before acting on it.
- The analysis reads the text: very short reviews ("Great!") give a sentiment but rarely a theme.
- Themes are checked in the review's own language; the output labels are in English.
- Links to each review (`reviewUrl`, `unansweredComplaintUrls`) come from the scraper. The Google Maps Reviews Scraper only gives them when its `personalData` option is on; everything else works without them.
- Typical speed: 250 reviews in about 15 seconds.

# Actor input Schema

## `reviews` (type: `array`):

Reviews to analyze: plain strings, or objects with text and optionally rating, date, placeName, category and reviewUrl. Reviews of several businesses are grouped by placeId or placeName. For the output of a scraper run, use datasetId instead.

## `datasetId` (type: `string`):

Read the reviews from an Apify dataset, for example the output of a Google Maps, Trustpilot or Tripadvisor reviews scraper. Text, rating, date, owner reply and business are detected automatically. When this Actor runs as an integration after a scraper, leave it empty: the dataset of the run that triggered it is used. The sample reviews are ignored when a dataset is given.

## `textField` (type: `string`):

Field holding the review text, dots for nested fields (default: text, reviewText, reviewBody, review, content...).

## `ratingField` (type: `string`):

Field holding the rating (default: stars, rating, reviewRating, ratingValue). Ratings out of 10 or 50 are converted to 5.

## `dateField` (type: `string`):

Field holding the publication date (default: publishedAtDate, publishedAt, datePublished, date).

## `replyField` (type: `string`):

Field holding the business owner's reply (default: responseFromOwnerText, ownerResponse, companyReply, reply).

## `placeIdField` (type: `string`):

Field used to group reviews by business (default: placeId, place.placeId, businessUnit.id, cid).

## `placeNameField` (type: `string`):

Field holding the business name (default: title, place.name, placeName, businessName, companyName).

## `categoryField` (type: `string`):

Field holding the business category, used to pick the themes (default: categoryName, categories, category).

## `businessType` (type: `string`):

Picks the themes checked in every review. Auto detects it from each business's category or name. Restaurant: food, service, waiting time, price, atmosphere, cleanliness. Hotel: room, cleanliness, staff, location, noise, breakfast, value. Health: reception, waiting time, listening, care quality, fees, appointments. Trades: punctuality, work quality, price and quote, honesty, communication.

## `themes` (type: `array`):

Themes to check in every review instead of the default ones, optionally with a description after a colon: "parking: parking space and access". Each theme comes back as praised or criticized.

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

Stop after this many reviews with a text. Default 500.

## `maxReviewsPerPlace` (type: `integer`):

Most recent reviews kept per business. Default 100.

## `includeReviewText` (type: `boolean`):

Copy the first 1,000 characters of each review into its result. Reviewer names are never copied.

## Actor input object example

```json
{
  "reviews": [
    {
      "text": "Lovely dinner, the duck was perfect and the staff were warm. We will come back.",
      "rating": 5,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Food was good but we waited 50 minutes for our mains and nobody apologized.",
      "rating": 3,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Overpriced for tiny portions. Our table next to the door was freezing.",
      "rating": 2,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Found a hair in my salad and the waiter just shrugged. Never again.",
      "rating": 1,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Great wine list and a cozy atmosphere, a bit noisy on Friday night.",
      "rating": 4,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    }
  ],
  "businessType": "auto",
  "maxReviews": 500,
  "maxReviewsPerPlace": 100,
  "includeReviewText": true
}
```

# Actor output Schema

## `results` (type: `string`):

One item per business: summary, counts, strengths, weaknesses, alerts and the analyzed reviews.

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

The same results unwound: one row per analyzed review, with its business.

# 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 = {
    "reviews": [
        {
            "text": "Lovely dinner, the duck was perfect and the staff were warm. We will come back.",
            "rating": 5,
            "placeName": "Sample Bistro",
            "category": "Restaurant"
        },
        {
            "text": "Food was good but we waited 50 minutes for our mains and nobody apologized.",
            "rating": 3,
            "placeName": "Sample Bistro",
            "category": "Restaurant"
        },
        {
            "text": "Overpriced for tiny portions. Our table next to the door was freezing.",
            "rating": 2,
            "placeName": "Sample Bistro",
            "category": "Restaurant"
        },
        {
            "text": "Found a hair in my salad and the waiter just shrugged. Never again.",
            "rating": 1,
            "placeName": "Sample Bistro",
            "category": "Restaurant"
        },
        {
            "text": "Great wine list and a cozy atmosphere, a bit noisy on Friday night.",
            "rating": 4,
            "placeName": "Sample Bistro",
            "category": "Restaurant"
        }
    ],
    "businessType": "auto",
    "maxReviews": 500,
    "maxReviewsPerPlace": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("sherwood/review-analyzer").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 = {
    "reviews": [
        {
            "text": "Lovely dinner, the duck was perfect and the staff were warm. We will come back.",
            "rating": 5,
            "placeName": "Sample Bistro",
            "category": "Restaurant",
        },
        {
            "text": "Food was good but we waited 50 minutes for our mains and nobody apologized.",
            "rating": 3,
            "placeName": "Sample Bistro",
            "category": "Restaurant",
        },
        {
            "text": "Overpriced for tiny portions. Our table next to the door was freezing.",
            "rating": 2,
            "placeName": "Sample Bistro",
            "category": "Restaurant",
        },
        {
            "text": "Found a hair in my salad and the waiter just shrugged. Never again.",
            "rating": 1,
            "placeName": "Sample Bistro",
            "category": "Restaurant",
        },
        {
            "text": "Great wine list and a cozy atmosphere, a bit noisy on Friday night.",
            "rating": 4,
            "placeName": "Sample Bistro",
            "category": "Restaurant",
        },
    ],
    "businessType": "auto",
    "maxReviews": 500,
    "maxReviewsPerPlace": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("sherwood/review-analyzer").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "reviews": [
    {
      "text": "Lovely dinner, the duck was perfect and the staff were warm. We will come back.",
      "rating": 5,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Food was good but we waited 50 minutes for our mains and nobody apologized.",
      "rating": 3,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Overpriced for tiny portions. Our table next to the door was freezing.",
      "rating": 2,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Found a hair in my salad and the waiter just shrugged. Never again.",
      "rating": 1,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    },
    {
      "text": "Great wine list and a cozy atmosphere, a bit noisy on Friday night.",
      "rating": 4,
      "placeName": "Sample Bistro",
      "category": "Restaurant"
    }
  ],
  "businessType": "auto",
  "maxReviews": 500,
  "maxReviewsPerPlace": 100
}' |
apify call sherwood/review-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,sherwood/review-analyzer"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WIn7y03PJ0rzd6zYv/builds/83IFmsFS2P0q0WkwG/openapi.json