Booking.com Reviews Scraper

Paste a Booking.com search or hotel URL — get guest reviews: scores, positive and negative feedback, reviewer details, keyword detection, and auto-detected themes. Export-ready dataset, no login needed.

Built for: Sentiment analysis · Hospitality quality audits · B2B lead generation · Competitive benchmarking

No login needed. No API key required.

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What does it extract?

Category	Fields
Hotel	Name, city, address, stars, avg score, total reviews
Review signal	Matched keywords, where they appeared (positive/negative/title), auto-detected themes
Review	Score, title, positive points, negative points, date, nights stayed, traveler type, room type, hotel reply
Reviewer	Name, country, language
Contact (optional)	Company name, email, phone, registration number, legal address

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Quick start

1. Go to booking.com, search for a city, apply filters (stars, dates, price…)
2. Copy the URL from your browser
3. Paste it into Search URL(s) in the Actor input
4. Set Reviews per hotel — 1 page ≈ 10 reviews (default: 2 pages)
5. Add keywords to focus on specific feedback (e.g. noise, bed, cleanliness) — or leave empty to collect all
6. Click Start — results appear in the dataset within seconds

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Input parameters

Parameter	Type	Default	Description
searchUrls	Array	—	Booking.com search results page URLs
hotelUrls	Array	[]	Direct hotel page URLs (bypasses search step)
maxHotels	Integer	0	Stop after this many hotels (0 = no limit)
maxReviewPages	Integer	2	Pages of reviews per hotel (~10 reviews/page)
reviewLang	String	""	Filter by review language: en, fr, de, es, it, nl, pt, pl, ru, zh, ja, ar — empty = all languages
order	String	completed_desc	Sort: newest first, oldest first, highest/lowest rated, featured
keywordFilter	Array	[]	Only keep reviews mentioning these words (whole-word, accent-insensitive)
enrichWithProHostContactDetails	Boolean	false	Add email, phone, SIRET and company name to each row

Input example — search mode

{
  "searchUrls": ["https://www.booking.com/searchresults.fr.html?ss=Paris&checkin=2026-05-01&checkout=2026-05-02"],
  "maxHotels": 20,
  "maxReviewPages": 3,
  "reviewLang": "fr",
  "keywordFilter": ["bruit", "literie", "propreté", "noise", "bed", "cleanliness"]
}

Input example — hotel URLs mode

{
  "hotelUrls": [
    "https://www.booking.com/hotel/fr/moxy-paris-bastille.html",
    "https://www.booking.com/hotel/fr/sofitel-arc-de-triomphe.html"
  ],
  "maxReviewPages": 10,
  "order": "score_asc",
  "enrichWithProHostContactDetails": true
}

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Keyword filtering

Enter keywords — a review is kept if any keyword appears as a whole word in the positive points, negative points, or title.

• Accent-insensitive: "decor" matches "décor"
• Whole-word only: "lit" matches "le lit" but not "literie" or "accessibility"
• Mix French and English for multilingual coverage
• Leave empty to collect all reviews

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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": "literie, lit",
  "keyword_matched_in": "positive",
  "themes": "chambre; rapport qualité-prix",
  "score": 9,
  "title": "Très bon séjour",
  "positive_points": "La literie est excellente, le lit très confortable.",
  "negative_points": "Rien à signaler.",
  "review_date": "2026-04",
  "stay_nights": 2,
  "traveler_type": "Couple",
  "room_type": "Chambre Double Supérieure",
  "lang": "fr",
  "reviewer_name": "Marie",
  "reviewer_location": "France",
  "partner_reply": "Merci pour votre retour ! Nous sommes ravis que la literie vous ait plu.",
  "scrape_date": "2026-04-15"
}

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Pricing (Pay Per Event)

Event	Price
1 review collected	$0.002
1 review with contact enrichment	$0.012

The run stops automatically when your spending limit is reached.

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Important / Limitations

• Review dates are YYYY-MM format — Booking.com does not expose the exact day in public review listings
• Contact enrichment relies on publicly available hotel page data — not all properties publish SIRET or email
• Language filter affects which reviews are returned, not the language of your keyword list — use keywords in both languages for best coverage

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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.

input.json example:

{
  "searchUrls": ["https://www.booking.com/searchresults.fr.html?ss=Nîmes&dest_id=-1455068&dest_type=city"],
  "maxHotels": 3,
  "maxReviewPages": 1,
  "keywordFilter": ["literie", "bruit", "bed", "noise"]
}

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Support

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