Booking.com Hotel Reviews Scraper

Pull guest reviews for a single property into structured rows: scores, pros/cons, stay context, and reviewer metadata—so you can use them in spreadsheets, dashboards, or support without copying from the site.

Who it’s for

• Guest experience & operations teams who need a snapshot of what guests say about one hotel.
• Revenue & brand stakeholders tracking sentiment and recurring themes for a property.
• Agencies reporting on a client or competitor listing.

What you can do with it

• Read many reviews at once for a known property ID.
• Choose how many reviews to collect so runs stay predictable.
• Optionally sort (when you already know a valid sort option from your Booking workflow) and language for the response.

How it works (in plain terms)

You provide the hotel (property) ID from your usual Booking data flow (for example after a hotel search). The tool pages through review results until it reaches your maximum number of rows or there are no more reviews on the next page.

Input

Field	Required	What it means
Hotel ID (hotel_id)	Yes	The property identifier (same as in search results for that hotel).
Maximum results (maxResults)	No	Upper limit on how many reviews to collect across pages (default 100).
Sort option (sort_option_id)	No	How reviews are ordered (e.g. most relevant or by date), when your workflow supplies a valid value.
Language (languagecode)	No	Response language (e.g. en-us).

Output

• One row per review per page, until your cap or the end of results.
• Identity & timing: review and property IDs, review date, review language, and guest country when present.
• Ratings & summary: overall score (average_score), short title, and flags such as moderation or anonymous-style fields when the platform returns them.
• Verbatim feedback: pros and cons (plus translated fields when empty or filled), and hotel response when the property has replied.
• Stay context: travel purpose, room name, check-in / check-out, nights, and room photo links under stayed_room_info when available.
• Reviewer profile: name, avatar, group type (e.g. friends, family), review counts, and helpful votes.
• Engagement: tags, reviewer-uploaded photos, helpful votes on the review.
• Every row adds when that record was written (scrapedAt) so you know which export run it came from.

Illustrative shape only: some reviews omit or shorten fields; image URLs are long and may expire.

Sample output (one dataset row, trimmed)

{
  "review_id": 5205721280,
  "hotel_id": 5955189,
  "title": "Pleasant",
  "date": "2025-09-02 05:24:15",
  "languagecode": "en-gb",
  "countrycode": "in",
  "average_score": 2.40000009536743,
  "travel_purpose": "other",
  "pros": "1.Whenever help was asked staff responded well…\n2.Professional behaviour of staff\n3.Assistance was provided in shifting luggage…",
  "cons": "1.Rooms are congested\n2.…bedsheets…\n3.…breakfast…\n4.…advance payment…",
  "pros_translated": "",
  "cons_translated": "",
  "title_translated": "",
  "hotelier_response": "",
  "tags": [],
  "reviewer_photos": [],
  "helpful_vote_count": 0,
  "stayed_room_info": {
    "room_name": "Deluxe Room",
    "room_id": 595518907,
    "checkin": "2025-08-29",
    "checkout": "2025-09-01",
    "num_nights": 3,
    "photo": {
      "photo_id": 613758060,
      "url_square60": "https://cf.bstatic.com/xdata/images/hotel/square60/613758060.jpg?…"
    }
  },
  "author": {
    "name": "Aneeta",
    "user_id": 958511072,
    "type_string": "Group of friends",
    "countrycode": "in",
    "avatar": "https://lh3.googleusercontent.com/a/ACg8ocItbpgTDUZ3z79qwU1o8VK2yZh7WHNo_QucX8MiZMKNHAjxmw=s96-c64"
  },
  "scrapedAt": "2026-03-25T05:49:29.224Z"
}

Good to know

• The hotel ID must match a real property; wrong IDs return empty or error responses.
• Review text and fields can vary by guest, language, and platform updates.
• Use data in line with Booking.com’s terms and the laws that apply to you.