Booking Reviews Scraper

Extract Booking.com hotel reviews at scale — negative feedback, scores, reviewer details — with optional contact info (email, phone, SIRET) for each property. Paste a search URL or a list of hotels, run, and get your data. Perfect for sentiment analysis, quality audits, and B2B outreach.

Last updated: February 22, 2026

---

Why Use This Scraper?

Save Time & Money

• Process hundreds of reviews in minutes instead of hours of manual reading
• No more copy-pasting from Booking.com pages
• Automate review collection for multiple hotels

Get Complete Information

• Reviewer profiles (name, location, review count)
• Scores (0-10) and rating text (e.g., "Very Good", "Très bien")
• Structured positive and negative feedback
• Hotel data (name, address, price in EUR, GPS, distance from center)
• Optional: company email, phone, SIRET for B2B prospecting

Reliable & Scalable

• Built-in error handling and retries
• Pagination support (1–50 pages per hotel, ~25 reviews per page)
• Keyword filtering to focus on specific topics
• Progressive output: reviews appear in the dataset as each hotel completes

---

Perfect For

• Hoteliers & property managers – Identify improvement areas from guest feedback
• Market researchers & analysts – Comparative analysis across hotels and destinations
• Travel agencies & OTAs – Data-driven property recommendations
• Real estate & property investors – Due diligence and performance assessment

---

How It Works

1. Input – Provide a Booking.com search URL, single hotel URL, or list of hotel URLs
2. Extract – The scraper visits each hotel, paginates through reviews, optionally fetches contact details
3. Output – Structured dataset (JSON) ready for Excel, Sheets, or BI tools — data appears progressively as hotels complete

---

Quick Start

Option 1: Search URL (Recommended)

Paste a Booking.com search results URL – the scraper extracts hotels automatically:

{
  "mode": "searchUrl",
  "searchUrl": "https://www.booking.com/searchresults.fr.html?ss=Paris&checkin=2026-03-15&checkout=2026-03-16",
  "maxPagesForReviews": 2,
  "maxHotels": 5
}

Option 2: Hotel URL(s)

{
  "mode": "hotelUrls",
  "hotelUrls": [
    "https://www.booking.com/hotel/fr/fesch.fr.html",
    "https://www.booking.com/hotel/fr/another-hotel.fr.html"
  ],
  "maxPagesForReviews": 5,
  "order": "completed_desc"
}

---

What You Get

Sample output record (with hotel + contact enrichment):

{
  "hotel_url": "https://www.booking.com/hotel/fr/fesch.fr.html",
  "hotel_name": "Hôtel Fesch & Spa",
  "hotel_price": 86.91,
  "hotel_price_currency": "EUR",
  "hotel_avg_score": "8.3",
  "hotel_review_count": "42",
  "reviewer_name": "Maevane",
  "reviewer_location": "France",
  "score": 8,
  "score_text": "Très bien",
  "traveler_type": "Couple",
  "room_type": "Chambre Double Standard",
  "stay_nights": 2,
  "review_date": "2026-01-16",
  "positive_points": "Emplacement parfait. Réservation tardive possible.",
  "negative_points": "Bruyant, température ambiante médiocre",
  "themes": "accueil; emplacement",
  "hotel_email": "reservation@hotel-fesch.com",
  "hotel_phone": "+330495516262",
  "hotel_company_name": "SCI FESCH",
  "hotel_registration_number": "81167178300025"
}

Output: Dataset items (JSON) — exportable as JSON, CSV, Excel, or XML directly from Apify.

---

Input Options

Option	Type	Default	Description
mode	select	searchUrl	searchUrl = paste a search page · hotelUrls = list of hotel pages
searchUrl	string	–	Booking.com search URL (city + dates). Used when mode = searchUrl.
hotelUrls	array	–	List of hotel page URLs. Used when mode = hotelUrls.
maxPagesForReviews	number	2	Review pages per hotel (~25 reviews/page). 1–50.
maxHotels	number	5	Max hotels from search (0 = unlimited). searchUrl mode only.
order	string	completed_desc	Sort: completed_desc, completed_asc, score_desc, score_asc, featuredreviews
reviewLang	string	fr	Filter by language: fr, en, de, es, it, pt, etc.
customerType	string	total	Traveler type filter: total, couple, family_with_children, etc.
enrichWithProHostContactDetails	boolean	false	Extract email, phone, SIRET for B2B prospecting
keywordFilter	array	[]	Keep only reviews containing these keywords in negative or positive points
extractionQuality	select	recommended	fast (more parallel, fewer retries) · recommended · thorough (slower, more retries)
antiBlocking	select	medium	low · medium · high — adds delay between requests to reduce blocking
proxyConfiguration	object	Residential FR	Native Apify proxy config. Residential recommended for best results.

---

Use Cases

Guest Feedback Analysis – Identify common complaints and improvement priorities from negative points.

Competitive Benchmarking – Compare scores and feedback across multiple hotels.

Market Research – Study rating trends, guest preferences, and sentiment by destination.

B2B Lead Generation – Extract hotel contact details (email, phone, SIRET) for outreach.

Trend Tracking – Monitor how ratings change over time with historical data.

Keyword-Focused Analysis – Filter reviews mentioning specific topics (e.g., cleanliness, breakfast, noise).

---

Complete Data Fields

Computed fields

Field	Description
hotel_id	Hotel slug (e.g. ibis-budget-orly) for deduplication
hotel_avg_score	Average score across all collected reviews for this hotel
hotel_review_count	Number of reviews for this hotel in the dataset
stay_nights	Parsed nights (e.g. "Stayed 3 nights" → 3)
themes	Auto-extracted themes: propreté, accueil, petit-déjeuner, chambre, emplacement, parking, bruit, équipement, rapport qualité-prix
hotel_reviews_url	Direct link to the reviews page
scrape_date	Date of extraction (YYYY-MM-DD)

Review Data (core)

Field	Description
hotel_url	Original hotel URL
reviewer_name, reviewer_location, reviewer_comments_count	Reviewer info
score, score_text	Rating (0–10 and text)
traveler_type, room_type, travel_type	Stay metadata
review_date	Date review was posted
positive_points, negative_points	Feedback content
matched_keywords	Keywords from filter found in this review
keyword_excluded_detected	Filter keywords not found in this review

Hotel Enrichment (searchUrl mode)

Field	Description
hotel_name, hotel_address, hotel_city	Identity
hotel_latitude, hotel_longitude	GPS
hotel_distance_center_km	Distance from city center
hotel_price, hotel_price_currency	Price for search dates (always EUR)

Contact Enrichment (enrichWithProHostContactDetails)

Field	Description
hotel_company_name	Legal company name
hotel_email, hotel_phone	Contact
hotel_registration_number	SIRET (France) or equivalent
hotel_contact_address, hotel_contact_city, hotel_contact_postal_code, hotel_contact_country	Business address

---

Before vs. After

Without Scraper	With Scraper
2–3 hours to manually extract 50 reviews	5–10 minutes for 100+ reviews
Selective, error-prone extraction	16+ fields per review, structured
Copy-paste into spreadsheets	JSON/CSV/Excel directly from Apify

---

Costs and Optimization

Testing (no proxy, 3 hotels, 2 pages): ~$0.01
Production (residential proxy, 10 hotels, 5 pages): ~$0.35
Tip: Start with maxHotels: 5 and maxPagesForReviews: 1 to validate, then scale up.

---

Getting Started

1. Prepare input: Get a Booking.com search URL (search a city with dates) or hotel URL(s)
2. Configure: Paste URL in Apify input, set maxPagesForReviews and maxHotels as needed
3. Run: Start the actor and monitor logs — data appears progressively per hotel
4. Export: Download from the Dataset tab (JSON, CSV, Excel, XML)

Local Run

cd scrapers/booking-reviews-scraper
npm install
npm start

Uses input.json for configuration.

---

Support

For help or customization: corentin@outreacher.fr