# Booking.com Hotel Reviews Scraper (`dbott23/booking-reviews-scraper`) Actor

Extract guest reviews from Booking.com hotel pages — reviewer name, score, title, positive/negative text, room type, trip purpose, and date. WAF bypass built in. No proxy setup needed.

- **URL**: https://apify.com/dbott23/booking-reviews-scraper.md
- **Developed by:** [Darren S](https://apify.com/dbott23) (community)
- **Categories:** Travel, E-commerce
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 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 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

## Booking.com Hotel Reviews Scraper

Extract guest reviews from any Booking.com hotel page — reviewer name, score, review title, positive and negative text, room type, trip purpose, and date — all in a clean structured dataset ready for export or analysis.

Pass one URL or a hundred. The actor handles Booking.com's bot protection automatically and exits cleanly with your data.

***

### Features

- **Bot protection handled** — runs inside a real virtual-display browser (camoufox + Xvfb) that passes Booking.com's AWS WAF challenge without any manual steps
- **Full review fields** — name, country, score out of 10, review title, positive text, negative text, room booked, trip purpose, and stay date, all normalised
- **Multi-hotel batching** — supply a list of hotel page URLs and the actor scrapes them all in one run
- **Residential proxy built in** — no proxy configuration needed; Apify's residential pool is used automatically
- **Cheap and fast** — each hotel typically completes in under 60 seconds

***

### How to use

1. Open any hotel page on Booking.com — for example:
   `https://www.booking.com/hotel/us/hilton-new-york.html`
2. Paste the URL into the **Hotel URLs** field
3. Choose how many reviews you want, what order to sort them in, and which language
4. Click **Start** and your dataset will be ready in under a minute

The URL format `https://www.booking.com/hotel/{country}/{hotel-name}.html` works for every country and property on the platform.

***

### Input

| Field | Type | Default | Description |
|---|---|---|---|
| `hotelUrls` | string\[] | *(required)* | One or more Booking.com hotel page URLs |
| `maxReviewsPerHotel` | integer | 50 | Maximum number of reviews to return per hotel (1–1000) |
| `sortBy` | string | `recent` | Sort order: `recent` · `highest` · `lowest` |
| `language` | string | `en-gb` | BCP 47 language filter — e.g. `en-gb`, `de`, `fr`. Leave blank for all languages. |

***

### Output

Each row in the dataset is one guest review:

```json
{
  "hotel_name": "New York Hilton Midtown",
  "hotel_url": "https://www.booking.com/hotel/us/hilton-new-york.html",
  "hotel_slug": "hilton-new-york",
  "hotel_country": "us",
  "reviewer_name": "Julia",
  "reviewer_country": "us",
  "score": 9,
  "review_title": "Comfort and convenience in the heart of midtown.",
  "positive": "Location, comfortable beds, great shower, restaurant credit",
  "negative": "Elevator were slow and seemed unresponsive",
  "review_date": "2026-04-26",
  "stay_info": "Leisure",
  "room_type": "Urban King Room with Sofa Bed",
  "helpful_count": null,
  "owner_response": null
}
```

#### Output fields

| Field | Description |
|---|---|
| `hotel_name` | Full hotel name as shown on Booking.com |
| `hotel_url` | Canonical hotel page URL (without language suffix) |
| `hotel_slug` | URL slug used by Booking.com for this property |
| `hotel_country` | Two-letter country code from the hotel URL |
| `reviewer_name` | Guest's display name |
| `reviewer_country` | Guest's home country code |
| `score` | Review score out of 10 |
| `review_title` | Review headline written by the guest |
| `positive` | What the guest liked |
| `negative` | What the guest didn't like |
| `review_date` | Date the review was submitted (YYYY-MM-DD) |
| `stay_info` | Trip purpose — e.g. Leisure, Business, Couple, Family |
| `room_type` | Room category the guest booked |
| `helpful_count` | Number of "helpful" votes (when available) |
| `owner_response` | Management response to the review (when present) |

***

### Common use cases

- **Sentiment analysis** — process positive/negative text in bulk to surface what guests love or dislike about a property
- **Competitor intelligence** — scrape reviews of competing hotels to benchmark service quality, room satisfaction, and pricing perception
- **Market research** — aggregate reviews across a destination to understand traveller priorities and seasonal patterns
- **Review monitoring** — pull recent reviews for a portfolio of properties on a schedule

***

### Notes

- **Review volume per hotel:** Booking.com's server-side render includes approximately 10 featured reviews per hotel page — their curated selection of the most recent, highest-quality reviews. These are the same reviews shown prominently to new visitors making a booking decision.
- **Multiple hotels:** Chain the actor with a search or list to scrape dozens of properties in one run; each runs independently so a single failure doesn't stop the batch.
- **Pricing:** Pay per review extracted at $1.00 per 1,000 reviews. A typical 10-review hotel run costs under $0.01.

# Actor input Schema

## `hotelUrls` (type: `array`):

List of Booking.com hotel page URLs to scrape reviews from (e.g. https://www.booking.com/hotel/us/the-standard-high-line.html).

## `maxReviewsPerHotel` (type: `integer`):

Maximum number of reviews to scrape per hotel (1–1000).

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

Order in which reviews are fetched.

## `language` (type: `string`):

BCP 47 language code for the review language filter (e.g. en-gb, de, fr). Leave blank for all languages.

## Actor input object example

```json
{
  "hotelUrls": [
    "https://www.booking.com/hotel/us/new-york-hilton-midtown.html"
  ],
  "maxReviewsPerHotel": 50,
  "sortBy": "recent",
  "language": "en-gb"
}
```

# Actor output Schema

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

Guest reviews with hotel name, reviewer, score, title, positive/negative text, room type, trip purpose, and date.

# 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 = {
    "hotelUrls": [
        "https://www.booking.com/hotel/us/new-york-hilton-midtown.html"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("dbott23/booking-reviews-scraper").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 = { "hotelUrls": ["https://www.booking.com/hotel/us/new-york-hilton-midtown.html"] }

# Run the Actor and wait for it to finish
run = client.actor("dbott23/booking-reviews-scraper").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 '{
  "hotelUrls": [
    "https://www.booking.com/hotel/us/new-york-hilton-midtown.html"
  ]
}' |
apify call dbott23/booking-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,dbott23/booking-reviews-scraper"
        }
    }
}

```

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/GAnBmahoPZJGPHaMk/builds/kEH6Bha9Jo4NGOIht/openapi.json