🏨 Booking.com Reviews Scraper — Extract Hotel Reviews with Full Reviewer Profiles

What does Booking.com Reviews Scraper do?

Booking.com Reviews Scraper extracts complete guest review data from any Booking.com hotel, apartment, or accommodation listing. Give it a hotel URL or a destination name — it returns structured review records with 24 fields per review, including review text (liked/disliked), guest scores, reviewer profiles, stay details, traveler type, and hotel owner responses.

No login required. No API key needed. Works with both direct hotel URLs and destination search queries.

Why use Booking.com Reviews Scraper?

• 2x more data than competitors — 24 fields per review including traveler type, owner response, reviewer loyalty level, and language filter that other scrapers don't expose
• Destination search built in — give it "Paris" or "Bali beachfront" and it finds hotels, then scrapes reviews — no need to manually collect hotel URLs first
• 25% cheaper — $0.0015 per review vs. the leading alternative at $0.002
• Reliable on Cloudflare — uses a full browser with stealth mode and session rotation, not brittle HTTP scraping that gets blocked
• Smart cutoff date — combined with "Newest First" sorting, the scraper stops at your cutoff date and doesn't waste compute retrieving older reviews

What data does Booking.com Reviews Scraper extract?

Field	Type	Description
hotel_name	string	Hotel / property name
hotel_url	string	Direct hotel page URL
hotel_id	string	Booking.com internal property ID
hotel_rating	number	Aggregate guest rating (0–10)
review_id	string	Unique review identifier
review_title	string	Review headline written by guest
review_positive	string	"Liked" section — what guests praised
review_negative	string	"Disliked" section — what guests criticized
review_score	number	Individual review score (0–10)
review_score_label	string	Score label (Superb, Very Good, etc.)
review_date	string	Date review was published
date_of_stay	string	Month + year guest actually stayed
length_of_stay	integer	Number of nights at property
room_type	string	Room category (e.g. Superior Double)
traveler_type	string	Couple / Solo / Family / Business / Group
reviewer_name	string	Guest display name
reviewer_country	string	Guest country of origin
reviewer_review_count	integer	Total reviews written by this guest
review_language	string	ISO 639-1 language of the review text
helpful_votes	integer	Number of "helpful" votes on the review
owner_response	string	Hotel management response text
owner_response_date	string	Date management posted their response
scraped_at	string	ISO 8601 scrape timestamp
source_url	string	Source hotel page URL

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How to use Booking.com Reviews Scraper

Mode 1: Direct hotel URLs

1. Go to a Booking.com hotel page and copy the URL
2. Paste it into Hotel URLs in the input form
3. Set your review limit and filters
4. Click Start

Example URLs:

https://www.booking.com/hotel/cz/jeromehouse.en-gb.html
https://www.booking.com/hotel/us/the-plaza.en-gb.html
https://www.booking.com/hotel/fr/le-marais.en-gb.html

You can add multiple hotel URLs to scrape reviews from several properties in one run.

Mode 2: Destination search

1. Type a destination in the Search Query field (e.g. "Paris", "Bali beachfront", "New York Manhattan hotels")
2. Optionally set check-in/check-out dates to filter by availability
3. The scraper finds up to 30 hotels matching your query, then scrapes reviews from each

Combining both modes

You can provide both startUrls and searchQuery simultaneously — the scraper will process all hotel URLs from both inputs.

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API usage

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('USERNAME/booking-reviews-scraper').call({
    startUrls: [
        { url: 'https://www.booking.com/hotel/cz/jeromehouse.en-gb.html' }
    ],
    maxReviewsPerHotel: 200,
    sortReviewsBy: 'f_recent_desc',
    filterByTravelerType: 'couple',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("USERNAME/booking-reviews-scraper").call(run_input={
    "startUrls": [
        {"url": "https://www.booking.com/hotel/cz/jeromehouse.en-gb.html"}
    ],
    "maxReviewsPerHotel": 200,
    "sortReviewsBy": "f_recent_desc",
    "filterByTravelerType": "couple",
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

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Review filter options

Sort reviews by

Value	Description
f_relevance	Most relevant (Booking.com default)
f_recent_desc	Newest first — best for cutoff date
f_recent_asc	Oldest first
f_score_desc	Highest score first
f_score_asc	Lowest score first (find negative reviews)

Filter by score

Value	Description
f_all_reviews	All scores (default)
review_adj_superb	Superb: 9+
review_adj_good	Good: 7–9
review_adj_average_passable	Passable: 5–7
review_adj_poor	Poor: 3–5

Filter by traveler type

couple, solo_traveller, family_with_children, group_of_friends, business_traveler

Filter by language

Use ISO 639-1 codes: en, de, fr, es, it, nl, pt, ru, zh, ja, ko, ar, etc.

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Use cases

Reputation monitoring — Track what guests say about your property or competitors over time. Identify recurring complaints before they compound.

Competitive intelligence — Scrape reviews from 10–30 competing hotels in a destination. Find the gaps between what they promise and what guests experience.

Sentiment analysis + AI pipelines — Feed structured review data (positive/negative text, scores, traveler types) into LLM or NLP pipelines for automated sentiment classification.

Market research — Understand what drives high scores vs. low scores in a specific hotel category or location.

Fake review detection — Cross-reference reviewer_review_count, reviewer_country, and reviewer_name patterns to flag suspicious review clusters.

OTA content aggregation — Build review dashboards that aggregate Booking.com guest feedback alongside TripAdvisor, Google, and Expedia reviews.

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Pricing

This actor uses pay-per-event (PPE) pricing — you only pay for results you actually receive.

Event	Price
Actor start	$0.00005 (flat fee)
Per review scraped	$0.0015

Cost examples:

• 100 reviews from 1 hotel → ~$0.15
• 1,000 reviews from 10 hotels → ~$1.50
• 10,000 reviews → ~$15.00

You can set a spend cap in Actor → Settings → Budget to prevent unexpected costs during development.

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Tips for best results

Use "Newest First" + Cutoff Date to efficiently track only new reviews since your last run. This is the most cost-efficient approach for ongoing monitoring.

Residential proxies improve success rate on Cloudflare-protected pages. If you hit frequent blocks, switch from "Apify Proxy (datacenter)" to "Apify Proxy (residential)" in the proxy settings.

Large review counts (1000+) work fine — the scraper paginates automatically in batches of 25. For very large hotels, expect runs of 5–20 minutes.

International reviews — Use filterByLanguage to scrape only reviews in your target language, saving both time and cost.

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Output sample

{
  "hotel_name": "Jerome House Prague",
  "hotel_url": "https://www.booking.com/hotel/cz/jeromehouse.html",
  "hotel_id": "jeromehouse",
  "hotel_rating": 9.1,
  "review_id": "review_abc123",
  "review_title": "Absolutely perfect stay",
  "review_positive": "The breakfast was excellent and the staff went above and beyond. The location right in the old town was ideal for exploring Prague on foot.",
  "review_negative": "The room was slightly small but perfectly functional.",
  "review_score": 9.6,
  "review_score_label": "Superb",
  "review_date": "January 5, 2026",
  "date_of_stay": "December 2025",
  "length_of_stay": 3,
  "room_type": "Superior Double Room",
  "traveler_type": "Couple",
  "reviewer_name": "Sarah K.",
  "reviewer_country": "United Kingdom",
  "reviewer_review_count": 14,
  "review_language": "en",
  "helpful_votes": 6,
  "owner_response": "Thank you so much for your kind words! We're delighted you enjoyed your time with us in Prague.",
  "owner_response_date": "January 7, 2026",
  "scraped_at": "2026-04-13T10:30:00.000Z",
  "source_url": "https://www.booking.com/hotel/cz/jeromehouse.html"
}

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Integrations

Booking.com Reviews Scraper integrates with the full Apify ecosystem. Connect it with Make, Zapier, Slack, Google Sheets, Google Drive, Airtable, or any tool via webhooks and the Apify API. Export scraped data as JSON, CSV, Excel, or XML from your dataset.

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Legal disclaimer

This actor is intended for lawful data collection from publicly available sources. Users are responsible for compliance with applicable laws, Booking.com's Terms of Service, and data protection regulations (GDPR, CCPA, etc.). Do not use this actor to scrape personal data without a legitimate legal basis. Reviewer names and nationalities are publicly displayed on Booking.com listings. If you are unsure whether your use case is compliant, consult your legal team.