Booking reviews data

Description

Access a comprehensive collection of over 8.4 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 8.4 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, Couple, King ... 8 Booking United States 0000000000000000 1779695550011 386874 xu Stayed in July 2023 Valley Motel Pittsburgh 2026-05-24 02:41:09 2023-07-29 13:09:33 UTC 2026-05-25 07:52:30 Good place to stay overnight 7.8 0 2571 Freeport Road, Harmarv... John https://www.booking.com United States \N Location, cost
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... 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...
https://www.booking.com/hot... Leisure trip, Couple, Delux... 10 Booking United States 000004a0a23e3dc2 1779695550011 270499 xu Stayed in September 2025 Shasta Inn 2026-05-23 05:28:37 2025-10-07 15:19:27 UTC 2026-05-25 07:52:30 We stayed here twice this p... 8.4 0 1121 Mount Shasta Boulevard... Barbara https://www.booking.com USA \N Really nice location, clean...
https://www.booking.com/hot... Leisure trip, Family with y... 8 Booking United States 000005e637b1b05b 1779695550011 327898 xu Stayed in May 2024 Days-Inn by Wyndham Montgom... 2026-05-24 06:07:05 2024-05-10 22:42:39 UTC 2026-05-25 07:52:30 Very good 6.7 \N 5836 Monticello Drive, Mont... Mckenzie https://www.booking.com USA \N \N
https://www.booking.com/hot... Leisure trip, Couple, King ... 3 Booking United States 0000065339f1b3f9 1779695550011 266972 xu Stayed in April 2025 Radisson Hotel Cedar Rapids 2026-05-24 16:08:16 2025-05-11 02:27:14 UTC 2026-05-25 07:52:30 Will not be staying there a... 6.9 0 1200 Collins Rd NE, Cedar R... Shelly https://www.booking.com United States The heat was not working in... It was clean.
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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 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.

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Frequently Asked Questions

This Booking reviews dataset is updated frequently, typically on a daily basis, to ensure the freshest available information. The `scraped_at` timestamp for each record indicates the exact time of data collection.

The data is primarily delivered in flexible formats such as CSV or JSON. You can access it via secure file transfer, API integration, or through direct database exports.

Yes, you can customize your data pull by selecting specific fields from the available data points, like `hotel_name` or `positive_review_text`. Advanced filtering, such as by `reviewed_at` date ranges or `reviewer_country`, is also supported.

This dataset is invaluable for sentiment analysis, market research, competitor intelligence, and understanding customer satisfaction in the hospitality sector. Hotel chains, travel agencies, and data analytics firms can derive significant insights.

Yes, a representative sample of the dataset can be provided to help you evaluate its content and structure. We offer comprehensive support for integration, data queries, and any technical assistance you may require.