Description

Unlock nearly 50 million historical Booking.com hotel reviews and guest ratings, collected from 2010 to 2024. This extensive dataset provides unparalleled insights into global traveler experiences across countless properties. Each record features rich details including positive and negative review texts, individual ratings, average hotel scores, reviewer demographics, and specific hotel attributes like address and country. Ideal for comprehensive sentiment analysis, identifying emerging market trends, competitive benchmarking, and training advanced AI/ML models. This invaluable resource empowers travel & hospitality businesses, researchers, and data scientists to deeply understand guest satisfaction and industry dynamics directly from Booking.com.

Highlights

  • Nearly 50 million Booking.com reviews.

  • Global Booking.com hotel review coverage.

  • Reviews from 2010 to 2024.

  • Includes full review text, sentiment, ratings.

Sample Data

Preview of available data:

Url Tags Rating Source Country Uniq Id Version Hotel Id Language Stayed At Hotel Name Scraped At Reviewed At Imported At Review Title Average Score Helpful Count Hotel Address Reviewer Name Source Domain Reviewer Country Negative Review Text Positive Review Text
https://www.booking.com/hot... Leisure trip, Couple, Chamb... 9 Booking France 0000000000000000 1780462723111 1269749 fr Stayed in July 2023 HĂŽtel Restaurant du PĂȘcheur 2026-05-29 00:11:02 2023-07-28 05:05:10 UTC 2026-06-03 04:58:43 Superb 8.8 \N Place des Anciens Moulins, ... StĂ©phane https://www.booking.com France \N \N
https://www.booking.com/hot... Leisure trip, Family with y... 8 Booking France 00000015a2b0e747 1780462723111 1052044 fr Stayed in February 2024 HĂŽtel & Spa Les Carrettes 2026-05-26 16:32:52 2024-03-08 12:43:04 UTC 2026-06-03 04:58:43 Very good 8 \N Les Islettes, 73450 Valmein... Mathieu https://www.booking.com France \N \N
https://www.booking.com/hot... Leisure trip, Couple, Queen... 5 Booking United States 000000768359893c 1779695550011 1911599 de Stayed in May 2023 Arlo Williamsburg 2026-05-21 00:52:05 2023-05-23 16:44:28 UTC 2026-05-25 07:52:30 Passable 8.1 \N 96 Wythe Avenue, Brooklyn, ... Alice https://www.booking.com United States \N \N
https://www.booking.com/hot... Leisure trip, Solo traveler... 10 Booking United States 00000219f5cd1f60 1779695550011 11624707 xu Stayed in July 2024 Monterey Hostel 2026-05-23 13:27:47 2024-08-03 14:57:44 UTC 2026-05-25 07:52:30 I enjoyed being there! 8.8 0 778 Hawthorne Street, Monte... Khazaei https://www.booking.com Canada My room was in the basement... It was big and clean! With ...
https://www.booking.com/hot... Leisure trip, Family with y... 5 Booking France 000002d366eebe17 1780462723111 50961 it Stayed in August 2025 Kyriad Hotel Clermont Ferra... 2026-05-29 00:30:50 2025-08-15 19:05:51 UTC 2026-06-03 04:58:43 Passable 7.9 \N 51, Rue Bonnabaud, 63000 Cl... Jessica https://www.booking.com Italy \N \N
https://www.booking.com/hot... Leisure trip, Family with y... 1 Booking France 000002e96ef0723c 1780462723111 57715 fr Stayed in July 2025 Séjours & Affaires Poitiers... 2026-05-30 22:59:33 2025-07-06 19:17:48 UTC 2026-06-03 04:58:43 Bad 7.8 0 14 Boulevard Du Pont Achard... Claude https://www.booking.com Frankreich Pas de Clim beaucoup trop d... L emplacement
https://www.booking.com/hot... Leisure trip, Family with y... 3 Booking United States 000002f01110e4e7 1779299255461 428305 xu Stayed in June 2025 La Quinta by Wyndham Charlo... 2026-05-18 15:58:40 2025-06-27 19:27:23 UTC 2026-05-20 17:47:35 Poor 5 \N 4900 South Tryon Street, Ch... Milton https://www.booking.com USA \N \N
https://www.booking.com/hot... Leisure trip, Family with y... 1 Booking United States 000003ebf1c06d99 1779299255461 465667 fr Stayed in May 2024 Grand Canyon Inn and Motel ... 2026-05-17 20:28:09 2024-05-11 16:36:02 UTC 2026-05-20 17:47:35 Horrible 7 1 257 South State Route 64, V... Lea https://www.booking.com France Le personnel est horrible, ... Rien, hotel horrible \nEmpl...

Data Fields

This dataset includes the following data points:

Hotel Id
Url
Hotel Name
Hotel Address
Country
Average Score
Review Title
Reviewer Name
Rating
Reviewer Country
Negative Review Text
Positive Review Text
Helpful Count
Reviewed At
Stayed At
Tags
Source
Source Domain
Language
Uniq Id
Scraped At
Version
Imported At

Why This Data

This hotel reviews dataset from Booking provides comprehensive market intelligence and competitive insights. Perfect for:

  • Market Research: Understand market trends and customer preferences
  • Competitive Analysis: Compare pricing, products, and strategies
  • Business Intelligence: Make data-driven decisions
  • Price Monitoring: Track price changes and optimize your pricing

Use Cases

This dataset is perfect for various applications:

Dynamic Pricing Optimization: Analyze historical Average Score, Rating, and Reviewed At trends to adjust hotel pricing in real-time for maximum occupancy and revenue on Booking.com.

Competitor Performance Benchmarking: Benchmark competitor hotels by analyzing their Average Score, Negative Review Text, and Positive Review Text to identify market gaps and service improvement areas on Booking.com.

Advanced Sentiment Analysis Model Training: Train machine learning models using the Negative Review Text and Positive Review Text to automate sentiment detection and categorize specific guest feedback topics for Booking.com listings.

Traveler Preference & Amenity Forecasting: Identify emerging traveler preferences and recurring pain points by analyzing Tags and themes in Positive Review Text/Negative Review Text across Booking.com hotels to inform new product offerings.

Targeted Content & Keyword Strategy: Extract popular keywords and phrases from Review Title, Negative Review Text, and Positive Review Text to optimize Booking.com hotel descriptions and generate engaging travel content for specific Reviewer Country audiences.

Get Access to This Dataset

Start using this dataset today. Available in CSV, JSON, and Excel formats with flexible access options.

Frequently Asked Questions

The dataset is regularly updated to continuously integrate new guest reviews and ratings, keeping the information current and extending coverage beyond 2024.

The dataset is commonly provided in flexible formats such as CSV or JSON. Delivery is typically facilitated through secure cloud storage, API access, or direct download for convenience.

Yes, the dataset can be customized by filtering based on criteria like country, date range, or specific review attributes. While the listed fields are comprehensive, requests for additional or custom data points can be explored.

This dataset is ideal for sentiment analysis, competitive benchmarking, trend analysis in hospitality, and enhancing predictive models. Hoteliers, market researchers, data scientists, and travel technology companies are key beneficiaries.

Yes, a representative data sample is available for assessment. Comprehensive support, including technical assistance and data guidance, is typically offered to ensure successful integration and use.