kestrel actors › Guest & customer reviews

Booking.com Reviews Scraper — Hotel Guest Reviews & Ratings

A booking.com reviews scraper — a hotel review scraper for booking.com hotel reviews — that returns every guest review a property has, as structured rows: booking.com ratings on the 1–10 scale and out of five, the title, what the guest liked and what they disliked, the language it was written in, posting and stay dates, room, traveller type, reviewer name and country, guest photos and the property's public reply.

Run Booking.com Reviews Scraper on Apify →

Booking.com reviews scraper pricing: what one row costs

You pay per delivered review row. Everything else is free: hotel rows, status rows, reviews dropped by maxRating or requireText, properties with no reviews, and lookups that fail. The number of reviews you asked for is the number you pay for, capped by maxReviewsPerHotel and by whatever spending limit you set on the run. A 50-review complaints feed across ten properties is at most 500 review rows a day and usually far fewer, because most properties have few new complaints.

Booking.com reviews scraper input reference

Every field of Booking.com Reviews, straight from the actor's input schema.

Example input

{
  "startUrls": ["https://www.booking.com/hotel/pt/memmo-alfama.html", "https://www.booking.com/hotel/pt/the-lumiares.en-gb.html"],
  "maxReviewsPerHotel": 50,
  "reviewsSort": "most_recent",
  "maxRating": 6,
  "requireText": true
}

{
  "hotelNames": ["Memmo Alfama Lisbon", "The Lumiares Lisbon", "Hotel Valverde Lisboa"],
  "maxReviewsPerHotel": 0,
  "languages": ["en"],
  "requireText": true
}

Booking.com reviews scraper output fields

One row per guest review (charged), plus a free hotel context row and a free status row per property.

Dataset views in the Apify Console: Overview, Reviews, Complaints, Hotels, Status.

Example row from a real run

Taken from the actor's README — this is the shape your dataset holds.

{
  "type": "review",
  "url": "https://www.booking.com/hotel/pt/memmo-alfama.html",
  "locale": "en-US",
  "review_id": "c90f61915f999a43",
  "rating": 10.0,
  "rating_5": 5.0,
  "title": "Best place to stay in Lisbon!",
  "positives": "Great location and amazing terrace.",
  "negatives": null,
  "text": "Great location and amazing terrace.",
  "language": "en",
  "review_date": "2026-08-28",
  "check_in": "2026-08-24",
  "check_out": "2026-08-27",
  "nights": 3,
  "room_type": "Premium Double or Twin Room",
  "room_id": "53625108",
  "traveler_type": "Couple",
  "traveler_type_code": "COUPLES",
  "stay_status": "stayed",
  "reviewer_name": "Jean",
  "reviewer_country": "Canada",
  "reviewer_country_code": "ca",
  "anonymous": false,
  "helpful_votes": 0,
  "photos": null,
  "approved": true,
  "response": null,
  "hotel_id": "536251",
  "hotel_name": "Memmo Alfama - Design Hotels",
  "fetched_at": "2026-08-29T10:12:04+00:00"
}

Booking.com reviews scraper in Python, JavaScript and curl

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("kestrel/booking-reviews-scraper").call(run_input={
    "startUrls": ["https://www.booking.com/hotel/pt/memmo-alfama.html"],
    "maxReviewsPerHotel": 100,
    "reviewsSort": "most_recent",
})
reviews = [r for r in client.dataset(run["defaultDatasetId"]).iterate_items() if r["type"] == "review"]
for r in reviews[:5]:
    print(r["rating"], r["traveler_type"], r["title"], "|", r["negatives"])

JavaScript (Node)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('kestrel/booking-reviews-scraper').call({
    hotelNames: ['Memmo Alfama Lisbon'], maxReviewsPerHotel: 0, maxRating: 6, requireText: true,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
const complaints = items.filter(r => r.type === 'review');
console.log(complaints.length, 'complaints;', complaints.filter(r => r.response).length, 'answered by the hotel');

curl

curl -X POST "https://api.apify.com/v2/acts/kestrel~booking-reviews-scraper/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"hotelIds": ["536251"], "maxReviewsPerHotel": 25, "languages": ["en"]}'

The same three calls are runnable files in the examples repository: examples/booking-reviews-scraper.py|.js|.sh.

Limits and honest notes

FAQ

Does it need a Booking.com API key or login?

No. There is no public Booking.com reviews API for reading reviews; the actor uses the request Booking's own review list makes, which needs no key, no cookies and no browser.

Can I download Booking.com reviews as CSV or Excel?

Yes. Every run writes an Apify dataset; open the Reviews or Complaints view and export booking.com reviews csv, Excel, JSON or XML, or fetch the same through the API with ?format=csv.

How do I get only Booking.com negative reviews?

Set maxRating to 6 (Booking's own wording: 5 is "Poor", 7 is "Good"). The property is read lowest score first and paging stops at the threshold, so you pay only for the complaints. Add requireText: true to skip score-only ratings.

Do I need the hotel id, or can I use the URL?

Either — or the name. URLs and names are resolved through Booking's autocomplete and the matched name comes back in every row; ids skip the lookup. Once you have a hotel_id from a run, use it for the fastest repeat runs.

How many reviews can one hotel return?

All of them. Booking pages reviews 25 at a time and the actor keeps going until the property is exhausted or maxReviewsPerHotel is reached. A large city hotel with 15,000 reviews is 600 calls.

Are reviews translated, and which languages come back?

Reviews come back as written, with language set — multi language hotel reviews from one property, or booking.com reviews by language when you set languages. The hotel row lists every language the property's reviews come in, with counts, so you can pick what to fetch with languages. Booking's labels (traveller type, country names) follow locale.

Does it include the hotel's replies?

Yes — response carries the property's public reply when there is one.

What is traveler_type_code?

Booking's own guest category on the booking behind the review: COUPLES, FAMILIES, GROUP_OF_FRIENDS, SOLO_TRAVELLERS or BUSINESS_TRAVELLERS. traveler_type is the same thing in the site language.

Can I scrape Booking.com reviews without an API?

That is what this is — booking.com reviews without api keys: scrape booking.com reviews by URL, name or id, no key, no browser, pay per review.

Can I use it for hotel sentiment analysis?

Yes. positives and negatives are already split, text joins them for a single column, every row is tagged with language, rating and traveler_type, and requireText keeps the corpus clean.

What does bulk work cost?

Reviews only. A bulk hotel reviews pull of one hundred properties at 200 reviews each is at most 20,000 review rows; with requireText on it is typically 12,000, because score-only reviews are free.

How to use Booking.com Reviews Scraper

• How to Scrape Booking.com Reviews Without a Browser — Booking.com hotel pages need a browser and 60 seconds each. The review endpoint behind them does not. Here is the difference, with working Python.
• Which Review Sites Can Be Scraped Without a Browser — Measured evidence for 14 review sources: which answer a plain HTTP client, which need a TLS fingerprint, which need a browser, and which are closed.
• How to Scrape Booking.com Hotel Prices by Date — Stay totals, taxes, cancellation and room rates for a destination or a comp set — and the per-session pricing that makes naive comparisons wrong.
• How to Monitor Hotel Reputation Across OTAs — A daily feed of new guest reviews from every OTA a hotel sells on, normalised to one scale, at a cost measured in cents per property per month.
• How to Build a Voice of Customer Dataset — Join reviews from OTAs, marketplaces, app stores and Trustpilot into one table: scales, languages, dedupe keys, ceilings and what each source is good for.

Ready-made runs you can open and start

Each one is a saved input on the Apify Store — open it, press Start, and it runs with the fields already filled in. All 163 across the suite.

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• TripAdvisor Reviews Scraper — TripAdvisor hotel reviews with six sub-ratings, trip type, photos and the management response, in 30 site languages, no browser.
• Booking.com Scraper — Hotel Prices — Booking.com prices for exact dates and guests, for a whole destination or a hotel list: stay total, nightly rate, taxes, cancellation and breakfast flags, optional room rates.
• Agoda Reviews Scraper — Agoda hotel reviews with separate positives and negatives and the hotel's reply.
• Trip.com Reviews Scraper — Trip.com and Ctrip hotel reviews with four sub-scores and the Chinese-language corpus.
• Airbnb Reviews Scraper — every guest review of an Airbnb listing.
• Google Hotels Prices Scraper — hotel prices and every booking site's rate for a stay, Booking.com included.
• Hotel Rate Parity Checker — which OTAs undercut a property's direct rate.

Run Booking.com Reviews Scraper

The actor lives on the Apify Store, where the pricing tab carries the live per-row price and the input form has every field above: apify.com/kestrel/booking-reviews-scraper