Booking.com Hotel Reviews Scraper

Extract the featured guest reviews Booking.com shows on any hotel page — reviewer name, country, score, review title, positive and negative text, room type, trip type, and date — as a clean structured dataset ready for export or analysis.

What you get: Booking.com surfaces a curated set of ~10 featured reviews on each hotel page — its selection of the most useful, recent reviews shown to travellers making a booking decision. This actor extracts those, fast and reliably. Booking does not expose a hotel's full review history to scrapers, so this returns the featured set, not every review ever written.

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

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Features

• The reviews that matter — Booking's curated ~10 featured reviews per hotel: the ones shown prominently to prospective guests
• Bot protection handled — runs inside a real virtual-display browser (camoufox + Xvfb) that passes Booking.com's AWS WAF challenge without any manual steps
• Rich review fields — name, country, score out of 10, review title, positive text, negative text, room booked, trip type, 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

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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 of the featured reviews to return (up to ~10), 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.

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Input

Field	Type	Default	Description
hotelUrls	string[]	(required)	One or more Booking.com hotel page URLs
maxReviewsPerHotel	integer	10	How many of the ~10 featured reviews to return per hotel (1–10)
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.

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Output

Each row in the dataset is one guest review:

{
  "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)

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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

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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.