Description
Access a comprehensive collection of over 75 million
Booking.com hotel reviews data
, meticulously scraped directly from Booking.com. Each record provides rich details including hotel ID, name, address, average score, and extensive review specifics such as Review Title, Reviewer Name, Rating, Reviewer Country, and crucially, both
positive and negative review texts
. Dive deep into guest experiences with granular insights like Helpful Count, Tags, Stayed At, and Language, all filterable by Reviewed At timestamps for targeted analysis. This indispensable resource is ideal for market researchers, sentiment analysts, hoteliers, and competitive intelligence teams aiming to understand guest satisfaction and industry trends. Leverage this powerful dataset to identify key sentiment drivers, monitor brand reputation, and gain
actionable insights
into the global hospitality landscape for data-driven decisions.
Highlights
Over 70 million Booking.com reviews.
Extensive global hotel review coverage.
Includes timestamps for review recency.
Rich insights: ratings, text, and helpfulness.
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, Family with y... | 9 | Booking | إسبانيا | 0000000000000000 | 1782244445465 | 2816895 | fr | Stayed in July 2023 | بي فري غرانادا | 2026-06-23 10:45:00 | 2023-07-15 23:27:34 UTC | 2026-06-23 19:54:05 | Allez y les yeux fermés, | 8.3 | 0 | Calle Tiburón, 2, Chana, 18... | Nafaa | https://www.booking.com | France | \N | Personnel au top ,fait tout... |
| https://www.booking.com/hot... | Leisure trip, Couple, Class... | 9 | Booking | United Kingdom | 00000013a005bd4f | 1780898753813 | 178052 | en | Stayed in September 2024 | The Golden Fleece Hotel, Th... | 2026-06-03 12:05:16 | 2024-09-20 20:17:21 UTC | 2026-06-08 06:05:53 | Great stay in a lovely coac... | 8.4 | 0 | 42 Market Place, Thirsk, YO... | Philip | https://www.booking.com | United Kingdom | \N | Super. Position and excelle... |
| https://www.booking.com/hot... | Leisure trip, Family with y... | 8 | Booking | France | 00000015a2b0e747 | 1780462723111 | 1052044 | fr | Stayed in February 2024 | Hôtel & 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, Family with y... | 6 | Booking | 일본 | 0000001912eb4a56 | 1782030741133 | 1281296 | xu | Stayed in May 2025 | 사쿠라 테라스 더 갤러리 | 2026-06-20 10:22:58 | 2025-06-09 06:45:24 UTC | 2026-06-21 08:32:21 | Overall good. We enjoyed th... | 8.8 | 0 | 601-8002 교토후, 교토, Minami-ku... | Erlie | https://www.booking.com | United States | Overall, the stay was good.... | Welcome drinks, breakfast. |
| 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, Couple, Doubl... | 9 | Booking | France | 000000788e891552 | 1780898753813 | 340605 | nl | Stayed in September 2024 | Zenitude Hôtel-Résidences M... | 2026-06-06 16:33:57 | 2024-09-15 19:05:54 UTC | 2026-06-08 06:05:53 | Superb | 7.3 | 0 | 50 Route de Port Royal des ... | Peter | https://www.booking.com | Netherlands | Niets. | De ligging, de grootte van ... |
| https://www.booking.com/hot... | Leisure trip, Solo traveler... | 8 | Booking | Αυστρία | 0000014e678979e0 | 1782030741133 | 409693 | de | Stayed in February 2025 | Hotel Orangerie | 2026-06-15 23:51:21 | 2025-02-23 12:17:56 UTC | 2026-06-21 08:32:21 | Very good | 8 | \N | Grieshofgasse 11, 12. Meidl... | Stefan | https://www.booking.com | Germany | \N | \N |
| https://www.booking.com/hot... | Leisure trip, Couple, Good ... | 7 | Booking | Almanya | 0000015953da7f20 | 1781377760816 | 61441 | de | Stayed in January 2026 | Good Morning+ Halle Leipzig | 2026-06-12 09:47:18 | 2026-01-09 21:08:09 UTC | 2026-06-13 19:09:20 | Ein gutes Hotel für eine Na... | 7.8 | 1 | Hotelstr.1, 06184 Halle an ... | Walter | https://www.booking.com | Germany | Dass die Sauna nur per Beza... | Gutes Frühstück und ein ger... |
Data Fields
This dataset includes the following data points:
Why This Data
This 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:
Competitive Pricing Strategy: Analyze competitor average scores, review volumes, and sentiment in specific markets to inform dynamic pricing adjustments and understand perceived value.
Market Demand & Competitor Benchmarking: Identify underserved market segments and benchmark competitor hotel performance by analyzing review themes, average scores, and volume across regions.
Granular Aspect-Based Sentiment Modeling: Train NLP models using explicit ratings and positive/negative review texts to perform granular sentiment analysis and extract specific aspects of hotel experiences.
Emerging Guest Preference Identification: Track recurring positive and negative themes in reviews over time to identify evolving guest preferences, popular amenities, and emerging travel trends for future product development.
Review-Driven Content & Keyword Optimization: Extract common guest questions, praised features, and concerns from reviews to inform SEO keyword strategies and generate highly relevant content for hotel marketing.
Get Access to This Dataset
Start using this dataset today. Available in CSV, JSON, and Excel formats with flexible access options.