Booking.com Reviews Scraper

Paste a Booking.com search or hotel URL — get every guest review: scores, positive and negative feedback, reviewer details, and auto-detected themes. Filter by keyword, language, or traveler type. Export-ready dataset in seconds.

No login. No API key. No Booking.com account.

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Also available

The Booking.com suite — pick the job:

Actor	Use when
Booking Hotel Search	Full hotel catalog by city or URL
Booking Pro Host	Hotel emails and company names
Booking Availability	Day-by-day rates and sold-out
Booking Price Monitor	Market rates across date windows
This Actor — Reviews	Guest scores and feedback

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Who is this for?

You are…	What you get
Hospitality brand or hotel group	A continuous feed of guest sentiment across your properties — flagged by topic
Revenue manager	Competitor reviews to benchmark quality perception against your own ratings
Market researcher	Aggregated guest feedback across a city or region for any date range
PR or reputation agency	Raw review data to monitor brand mentions, recurring complaints, or praise
B2B outreach team	Verified hotel contact details alongside review data — ready to import in your CRM
Data journalist	Tourism quality data, structured and filterable by keyword or language

Not sure which hotels to target? Use a Booking.com search URL with your filters already applied — the scraper picks up every hotel from the results page.

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Quick start

1. Go to booking.com and search for a city
2. Apply any filters (star rating, dates, price range…)
3. Copy the URL from your browser address bar
4. Paste it into Search URL(s) and click Start
5. Reviews appear in your dataset within seconds

Or skip the search: paste direct hotel page URLs into Hotel URL(s) if you already know which properties you want.

Store Try uses one hotel URL and 1 review page (~10 reviews). Set Reviews per hotel to 0 for a full export.

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Ready-made examples (published tasks)

Example	Best for
French guest reviews for a Paris hotel	Paris hotel reputation / quality audit
Lyon hotel reviews mentioning noise	Revenue / ops spotting recurring complaints
Marbella hotel reputation reviews	Resort reputation / PR monitoring
Newest reviews for one Paris hotel	Store Try / first run

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What you get

Every review becomes one clean row:

Category	Fields
Hotel	Name, city, address, star rating, average score, total review count
Review	Score, title, positive points, negative points, date (YYYY-MM), nights stayed, traveler type, room type, hotel reply
Reviewer	Name, country, language
Topic detection	Matched keywords (which ones, where they appeared), auto-detected themes
Contact (add-on)	Email, phone, company name, registration number — for outreach

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Filters

• Keyword filter — keep only reviews mentioning specific words (noise, bed, cleanliness, bruit, literie…). Matching is whole-word and accent-insensitive.
• Language filter — limit to a specific review language (English, French, German, Spanish…)
• Sort order — newest first, lowest rated first, highest rated first, or featured

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How much does it cost?

Standard run (reviews only)

	FREE	BRONZE	SILVER	GOLD
Run start	$0.05	$0.05	$0.05	$0.05
Per review	$0.002	$0.0016	$0.0012	$0.0008
100 reviews	~$0.25	~$0.21	~$0.17	~$0.13
1,000 reviews	~$2.05	~$1.65	~$1.25	~$0.85
5,000 reviews	~$10.05	~$8.05	~$6.05	~$4.05

With contact enrichment (add-on)

Add-on cost is charged per review row enriched, on top of the standard rate above.

	FREE	BRONZE	SILVER	GOLD
Per review enriched	$0.010	$0.008	$0.006	$0.004
100 reviews + contacts	~$1.25	~$1.01	~$0.77	~$0.53

Note: Contact info availability varies by hotel and country. Most hotels publish a phone number; email and company registration number are less consistent.

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Output sample

{
  "hotel_id": "moxy-paris-bastille",
  "hotel_url": "https://www.booking.com/hotel/fr/moxy-paris-bastille.html",
  "hotel_name": "Moxy Paris Bastille",
  "hotel_city": "Paris",
  "hotel_stars": 4,
  "hotel_avg_score": "8.5",
  "hotel_total_reviews": 2847,
  "matched_keywords": "bed, literie",
  "keyword_matched_in": "positive",
  "themes": "room; value for money",
  "score": 9,
  "title": "Great stay",
  "positive_points": "The bed was incredibly comfortable.",
  "negative_points": "Nothing to report.",
  "review_date": "2026-04",
  "stay_nights": 2,
  "traveler_type": "Couple",
  "room_type": "Superior Double Room",
  "lang": "en",
  "reviewer_name": "Marie",
  "reviewer_location": "France",
  "partner_reply": "Thank you for your kind words!",
  "scrape_date": "2026-04-15"
}

Download JSON, CSV, Excel, or HTML from the Dataset tab.

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All input parameters

Parameter	Type	Default	Description
searchUrls	string[]	[]	Booking.com search results URLs
hotelUrls	string[]	—	Direct hotel page URLs. Try prefill: one Paris hotel
maxHotels	integer	0	0 = no cap. Try: 1
maxReviewPages	integer	0	Pages per hotel (~10 reviews each). 0 = all. Try: 1
order	string	completed_desc	Newest / oldest / score
reviewLang	string	all	e.g. fr, en
keywordFilter	string[]	[]	Keep reviews mentioning these words
enrichWithProHostContactDetails	boolean	false	Email / phone add-on

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FAQ

• Official Booking API? No. Public hotel / review pages.
• Only ~10 reviews? Try sets maxReviewPages: 1. Use 0 for the full hotel.
• Keyword filter and billing? Matching runs after fetch — you are billed only for matching rows.
• Exact review day? Booking exposes YYYY-MM only.

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Important / Limitations

• Review dates are YYYY-MM format — Booking.com does not expose the exact day in public review listings
• Keyword filtering is applied after fetching — you are billed only for reviews that match your filter
• Language filter affects which reviews Booking.com returns, not the language of your keyword list — use keywords in both languages for multilingual coverage
• Contact enrichment relies on publicly available hotel page data — coverage varies by country

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Is it legal to scrape Booking.com reviews?

This actor only collects data that any visitor can see on Booking.com — public guest reviews, scores, and hotel information. No login, no personal data beyond what reviewers choose to share publicly.

As with any data project, make sure your use complies with applicable regulations (GDPR, local data laws). When in doubt, consult a legal professional.

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Local development

npm install
apify run --input-file=./input.json

Results are saved to storage/datasets/default/. A output.csv file (Excel-compatible, BOM-encoded) is generated in the project root.

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Support

Contact corentin@outreacher.fr for custom scrapers, bulk runs, or tailored automation.