Booking.com Reviews Scraper - Guest Reviews & Scores

Bulk-pull guest reviews for any Booking.com hotel in one run - scores, what guests liked and disliked, reviewer country, room type, length of stay, and the hotel's reply. Type a hotel, set how many reviews you want, and export clean, structured rows. No login, no subscription, pay only per review.

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What this actor does

Give it a hotel (a slug like le-bristol-paris, a hotel name, or a Booking.com URL) and it paginates the property's guest reviews, pulling everything you need to analyze sentiment, spot patterns, and benchmark a property:

• 📊 Score - the guest review score (1-10) for each stay
• 👍 Liked / 👎 Disliked - the positive and negative free text, separated
• 🙋 Reviewer - name, country, traveller type (couple, solo, family, group)
• 🛏 Stay context - room type, number of nights, check-in date, season
• 🕒 Timing - the review date (converted from epoch to a clean YYYY-MM-DD)
• 👏 Engagement - helpful votes, and the hotel's partner_reply (in the data payload)

Need reviews for several hotels at once? Paste their URLs into Start URLs - every hotel's reviews land in the same dataset, ready to compare side by side.

Input

Field	What it does
Hotel	Slug, hotel name, or Booking.com hotel URL. Optional if you use Start URLs.
Start URLs	Paste several Booking.com hotel URLs to pull reviews for each.
Max reviews	Cap per hotel. The actor paginates Booking.com (up to 25/page) to reach it.
Sort	Most relevant, newest first, or oldest first.
Reviews per page	How many reviews to request per upstream page (1-25).
Filters	Guest type, score band, language, season of stay, free-text search.

Friendly-label filters (guest type, score band, season) map to Booking.com's own values for you - pick Families or Wonderful (9+), no codes to look up.

Output

One flat row per review, discriminated by row_type, with the full payload under data. The dataset ships with two ready-made table tabs - Overview and Reviews - so the data reads as a clean spreadsheet you can sort, filter and export to CSV/Excel/JSON in one click.

Common columns: score, title, positive_text, negative_text, reviewer_name, reviewer_country, traveller_type, room_type, num_nights, checkin_date, customer_type, review_date, language, helpful_votes, review_url. The data payload adds reviewer_country_code, reviewer_avatar, and the hotel's partner_reply. Error rows carry an error_code and are free - you only pay for real reviews.

A run-summary record (OUTPUT key) reports review counts and the estimated cost. Live per-event pricing is shown on this actor's Apify Store page.

Tip: combine score band and language to isolate a segment fast - for example only the English-language "Poor (3-5)" reviews to read exactly what is going wrong.

Why this actor

• Real reviews, real time - pulled live from Booking.com, with the hotel's own replies, not a stale snapshot.
• Positive and negative, separated - clean positive_text / negative_text columns, ideal for sentiment analysis.
• Every review filter - guest type, score band, language, season, text search. Friendly labels, no codes.
• Bulk across hotels - one hotel or a whole list, all in one dataset to compare.
• Clean tabs - an export-ready Reviews table, not a wall of mixed columns.
• You only pay for results - errors and your own misconfigurations are never charged.

More Booking.com tools

Part of our Booking.com suite - pick the actor that fits the job:

• Booking.com Hotels Scraper - search any destination for hotels with live prices, scores and full detail pages.
• Booking.com Airport Taxis Scraper - live airport transfer quotes between any two points.

Need help? Enterprise plans? Custom scraping?

I personally support every customer - drop a message on any channel.