# Hotel Review Intelligence & Competitor Sentiment Tracker (`scrapemint/booking-review-intelligence`) Actor
Extract and analyze hotel reviews from Booking.com. Get scores, sentiment text, reviewer demographics, room types, stay dates, and category breakdowns for any property. Built for hospitality operators and STR managers who want competitor intelligence without reading 1000 reviews manually.
- **URL**: https://apify.com/scrapemint/booking-review-intelligence.md
- **Developed by:** [Kennedy Mutisya](https://apify.com/scrapemint) (community)
- **Categories:** Travel, Real estate
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per usage
This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
## Booking.com Hotel Review Analysis and Competitor Sentiment Intelligence
Extract structured review data from any Booking.com property in minutes. Get review scores, positive and negative sentiment, reviewer demographics, room types, stay dates, category breakdowns, and management responses for hotels, apartments, hostels, and villas.
Built for hospitality operators, STR managers, and travel analysts who need guest sentiment data without reading thousands of reviews by hand.
---
### Who uses this and why
```mermaid
flowchart TD
A[Hotel Operators] -->|Track guest complaints
before they tank your score| D[Review
Intelligence]
B[STR Investors] -->|Benchmark sentiment
against competing properties| D
C[Revenue Managers] -->|Segment reviews by
traveler type and season| D
D --> E[Spot trends]
D --> F[Fix issues early]
D --> G[Price with confidence]
````
| Role | What you get |
|---|---|
| **Hotel operator** | Recurring complaint patterns (noise, cleanliness, value) ranked by frequency |
| **STR investor** | Side by side sentiment comparison of properties in a target market |
| **Revenue manager** | Review trends segmented by families, couples, solo, business travelers |
| **Travel analyst** | Structured review datasets ready for BI dashboards or sentiment models |
***
### How it works
```mermaid
flowchart LR
A[Booking.com
hotel URL] --> B[Load property page]
B --> C[Intercept GraphQL
review query]
C --> D[Paginate through
all reviews]
D --> E[Normalize and
structure data]
E --> F[(Download as
JSON, CSV, or Excel)]
```
The actor loads the Booking.com hotel page and intercepts the internal GraphQL query that powers the review section. It then paginates through reviews using the same API the Booking.com frontend uses. This returns richer data than page scraping: booking details, room types, check in and check out dates, management responses, helpful votes, and category score breakdowns.
***
### What one review record looks like
```json
{
"reviewScore": 7,
"title": "Excellent location, comfortable room and cozy atmosphere.",
"positiveText": "Ace hotel is located in a really good location, walking distance to multiple subway stops...",
"negativeText": "Room had strange smell at time, probably due to being stuffy.",
"reviewerName": "Tran",
"reviewerCountry": "Vietnam",
"travelerType": "Group",
"roomType": "Single",
"checkinDate": "2026-04-03",
"checkoutDate": "2026-04-06",
"numNights": 3,
"reviewedDate": "2026-04-12",
"helpfulVotes": 0,
"hasPartnerReply": false,
"categoryScores": [
{ "category": "Staff", "score": 8.6 },
{ "category": "Facilities", "score": 7.6 },
{ "category": "Cleanliness", "score": 7.9 },
{ "category": "Location", "score": 9.2 }
]
}
```
Every review includes: score (1 to 10), title, positive text, negative text, language, reviewer name, country, traveler type, room type, check in date, check out date, nights stayed, review date, helpful votes, whether management replied, reply text, photo count, and category scores (staff, facilities, cleanliness, comfort, value, location, wifi).
***
### Quick start
Extract the latest 200 reviews from a hotel:
```bash
curl -X POST "https://api.apify.com/v2/acts/scrapemint~booking-review-intelligence/run-sync-get-dataset-items?token=YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"hotelUrl": "https://www.booking.com/hotel/us/ace-new-york.html",
"maxReviews": 200
}'
```
Compare reviews across competing properties:
```json
{
"hotelUrls": [
{ "url": "https://www.booking.com/hotel/us/ace-new-york.html" },
{ "url": "https://www.booking.com/hotel/us/pod-39.html" }
],
"maxReviews": 500,
"travelerType": "BUSINESS_TRAVELLERS",
"sortBy": "NEWEST_FIRST"
}
```
***
### Inputs
| Field | Type | Default | What it does |
|---|---|---|---|
| `hotelUrls` | array | `[]` | Booking.com hotel page URLs. Add multiple to compare properties. |
| `hotelUrl` | string | `null` | Single URL shortcut. Used when hotelUrls is empty. |
| `maxReviews` | integer | `500` | Cap per hotel to control cost. |
| `sortBy` | string | `NEWEST_FIRST` | Options: `NEWEST_FIRST`, `OLDEST_FIRST`, `MOST_RELEVANT`, `SCORE_DESC`, `SCORE_ASC` |
| `travelerType` | string | `ALL` | Filter: `ALL`, `FAMILIES`, `COUPLES`, `SOLO_TRAVELLERS`, `GROUP_OF_FRIENDS`, `BUSINESS_TRAVELLERS` |
***
### Pricing
Pay per review extracted. Every run includes a free tier so you can verify data quality first.
| Tier | Price | Best for |
|---|---|---|
| Free | First 50 reviews per run | Testing the output format and data quality |
| Standard | $0.008 per review | Competitor benchmarking and trend tracking |
```mermaid
flowchart LR
A[Run the actor] --> B{First 50 reviews}
B -->|Free| C[Verify data quality]
C --> D{Need more?}
D -->|Yes| E[$0.008 per review
after the first 50]
D -->|No| F[Done, $0 spent]
```
***
### Cost comparison: Booking.com hotel review analysis
| Method | Cost for 1,000 reviews | Data depth | Time |
|---|---|---|---|
| Read them on Booking.com manually | 3 to 5 hours of analyst time | Notes in a spreadsheet | Hours |
| Review aggregator subscription | $50+ per property per month | Scores only, no text | Delayed |
| **This actor** | **$7.60 once** | Full text, metadata, scores, replies | Minutes |
***
### Data flow: from raw reviews to actionable intelligence
```mermaid
flowchart TD
subgraph Input
A[One or more
Booking.com URLs]
end
subgraph Processing
B[Load hotel pages]
C[Intercept review API]
D[Paginate all reviews]
E[Normalize fields]
end
subgraph Output
F[JSON / CSV / Excel]
end
subgraph Your workflow
G[Spreadsheet analysis]
H[BI dashboard]
I[Sentiment ML model]
J[Competitor report]
end
A --> B --> C --> D --> E --> F
F --> G
F --> H
F --> I
F --> J
```
***
### Related tools in the hospitality intelligence suite
```mermaid
flowchart LR
A[Airbnb Market
Intelligence] --> B[Booking Review
Intelligence]
B --> C[Hotel Pricing
Intelligence]
C --> D[(Full competitor report
$0.05 per property)]
```
- [**Airbnb Market Intelligence**](https://apify.com/scrapemint/airbnb-market-intelligence): live pricing, beds, ratings, and GPS coordinates for every Airbnb listing in a market
- **Hotel Pricing Intelligence**: nightly rate tracking across Booking.com properties (coming soon)
***
### Frequently asked questions
**How do I analyze Booking.com reviews without reading them one by one?**
Run this actor with a hotel URL and a review cap. It returns every review as a structured record with score, text, reviewer info, and category breakdowns. Export as JSON, CSV, or Excel and sort, filter, or visualize in any tool you already use.
**What data do I get per review?**
Score (1 to 10), title, positive text, negative text, language, reviewer name and country, traveler type, room type, check in and check out dates, number of nights, helpful vote count, whether management replied, the reply text, photo count, and category scores for staff, facilities, cleanliness, comfort, value, location, and wifi.
**Can I filter Booking.com reviews by traveler type?**
Yes. Set the `travelerType` input to get only reviews from families, couples, solo travelers, groups, or business travelers. Useful for understanding how different guest segments experience the same property.
**How many reviews can I pull from a single hotel?**
Up to the full review history. Set `maxReviews` to control the cap. Properties with 5,000+ reviews take a few minutes to finish.
**Does it work for apartments and hostels, not just hotels?**
Any property with a public review page on Booking.com. Hotels, apartments, hostels, villas, guesthouses.
**How fresh is the data?**
Live at query time. Every run pulls directly from Booking.com. No cached data, no stale monthly exports.
**Can I compare reviews across multiple hotels?**
Yes. Pass multiple URLs in the `hotelUrls` array. Each review record includes a `sourceUrl` field so you can group results by property in your analysis.
**What format is the output?**
JSON, CSV, or Excel. Download from the Apify dataset page or pull via API.
# Actor input Schema
## `hotelUrls` (type: `array`):
One or more Booking.com hotel page URLs. Example: https://www.booking.com/hotel/us/ace-new-york.html
## `hotelUrl` (type: `string`):
Single Booking.com hotel URL. Used only if hotelUrls is empty.
## `maxReviews` (type: `integer`):
Hard cap per hotel. Prevents runaway charges on properties with thousands of reviews.
## `sortBy` (type: `string`):
Review sort order.
## `travelerType` (type: `string`):
Only return reviews from this traveler segment.
## `proxyConfiguration` (type: `object`):
Apify proxy settings.
## Actor input object example
```json
{
"hotelUrls": [
{
"url": "https://www.booking.com/hotel/us/ace-new-york.html"
}
],
"maxReviews": 500,
"sortBy": "NEWEST_FIRST",
"travelerType": "ALL",
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"BUYPROXIES94952"
]
}
}
```
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"hotelUrls": [
{
"url": "https://www.booking.com/hotel/us/ace-new-york.html"
}
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"BUYPROXIES94952"
]
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapemint/booking-review-intelligence").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"hotelUrls": [{ "url": "https://www.booking.com/hotel/us/ace-new-york.html" }],
"proxyConfiguration": {
"useApifyProxy": True,
"apifyProxyGroups": ["BUYPROXIES94952"],
},
}
# Run the Actor and wait for it to finish
run = client.actor("scrapemint/booking-review-intelligence").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 '{
"hotelUrls": [
{
"url": "https://www.booking.com/hotel/us/ace-new-york.html"
}
],
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"BUYPROXIES94952"
]
}
}' |
apify call scrapemint/booking-review-intelligence --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapemint/booking-review-intelligence",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Hotel Review Intelligence & Competitor Sentiment Tracker",
"description": "Extract and analyze hotel reviews from Booking.com. Get scores, sentiment text, reviewer demographics, room types, stay dates, and category breakdowns for any property. Built for hospitality operators and STR managers who want competitor intelligence without reading 1000 reviews manually.",
"version": "0.1",
"x-build-id": "eNKRF5uQxQ7qZs7tP"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapemint~booking-review-intelligence/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapemint-booking-review-intelligence",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapemint~booking-review-intelligence/runs": {
"post": {
"operationId": "runs-sync-scrapemint-booking-review-intelligence",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapemint~booking-review-intelligence/run-sync": {
"post": {
"operationId": "run-sync-scrapemint-booking-review-intelligence",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"hotelUrls": {
"title": "Booking.com hotel URLs",
"type": "array",
"description": "One or more Booking.com hotel page URLs. Example: https://www.booking.com/hotel/us/ace-new-york.html",
"items": {
"type": "object",
"required": [
"url"
],
"properties": {
"url": {
"type": "string",
"title": "URL of a web page",
"format": "uri"
}
}
}
},
"hotelUrl": {
"title": "Single hotel URL (alternative)",
"type": "string",
"description": "Single Booking.com hotel URL. Used only if hotelUrls is empty."
},
"maxReviews": {
"title": "Maximum reviews to extract",
"minimum": 1,
"maximum": 10000,
"type": "integer",
"description": "Hard cap per hotel. Prevents runaway charges on properties with thousands of reviews.",
"default": 500
},
"sortBy": {
"title": "Sort reviews by",
"enum": [
"NEWEST_FIRST",
"OLDEST_FIRST",
"MOST_RELEVANT",
"SCORE_DESC",
"SCORE_ASC"
],
"type": "string",
"description": "Review sort order.",
"default": "NEWEST_FIRST"
},
"travelerType": {
"title": "Filter by traveler type",
"enum": [
"ALL",
"FAMILIES",
"COUPLES",
"SOLO_TRAVELLERS",
"GROUP_OF_FRIENDS",
"BUSINESS_TRAVELLERS"
],
"type": "string",
"description": "Only return reviews from this traveler segment.",
"default": "ALL"
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Apify proxy settings."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```