crawlfeeds/Booking-Hotel-Reviews-Dataset

Booking.com Hotel Reviews Dataset – 4.3K Sample

A rich, structured dataset of hotel reviews collected from Booking.com, featuring a unique split of positive and negative review text, reviewer country, stay dates, traveler tags, and hotel location data. Ideal for sentiment analysis, aspect-based opinion mining, travel AI, hospitality recommendation systems, and LLM fine-tuning on real-world review data.

Dataset Overview

Field	Details
Source	Booking.com
Total Records	4,300+
Category Focus	Hotel Reviews, Travel, Hospitality
Languages	Multilingual (language field included)
Formats	CSV / JSON
Data Quality	Validated and structured
Provider	Crawl Feeds

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What Makes This Dataset Valuable

• Split positive and negative review text — unlike most review datasets that provide a single text blob, this dataset separates positive and negative feedback into distinct fields, enabling precise aspect-based sentiment analysis and fine-grained opinion mining out of the box
• Traveler tags — structured tags per review (e.g. "Solo traveler", "Couple", "Family with kids", "Business trip") provide rich contextual labels for segmentation models and personalized recommendation systems
• Reviewer country — 99.9% coverage on reviewer nationality enables cross-cultural sentiment analysis and geo-targeted hospitality intelligence
• Stay date vs review date — both stayed_at and reviewed_at fields are present, enabling temporal analysis of review lag and seasonal sentiment patterns
• Hotel location data — hotel name, address, and country fields allow geographic clustering and location-based recommendation modeling
• Multilingual coverage — language field included, enabling multilingual NLP model training and evaluation
• Helpful count — social validation signal available in full dataset for review quality modeling

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Data Fields

Field	Type	Coverage	Description
url	String	100%	Direct review page URL
hotel_name	String	100%	Name of the reviewed hotel
hotel_address	String	100%	Full hotel address
country	String	100%	Country where the hotel is located
average_score	Float	—	Hotel average score (full dataset)
review_title	String	100%	Title of the review
reviewer_name	String	98.3%	Name of the reviewer
rating	Float	—	Numerical rating (full dataset)
reviewer_country	String	99.9%	Country of the reviewer
negative_review_text	String	35.9%	Reviewer's negative feedback
positive_review_text	String	55.8%	Reviewer's positive feedback
helpful_count	Int	—	Number of helpful votes (full dataset)
reviewed_at	String	100%	Date the review was submitted
stayed_at	String	100%	Date of the actual stay
tags	String	100%	Traveler type and trip context tags
source	String	100%	Source platform name
source_domain	String	100%	Source platform domain
language	String	100%	Language of the review
uniq_id	String	100%	Unique record identifier
scraped_at	String	100%	Data collection timestamp

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Use Cases

Sentiment Analysis & Opinion Mining

• Aspect-based sentiment analysis — use the positive/negative split to train models that identify sentiment by hotel aspect (cleanliness, location, service, value)
• Fine-grained opinion mining — the dual-text structure removes the need for manual sentiment labeling, providing ready-to-use positive/negative training pairs
• Zero-shot sentiment classification — use review titles and text for zero-shot benchmarking of LLMs on hospitality domain
• Multilingual sentiment models — leverage the language field to build and evaluate cross-lingual sentiment classifiers

Travel & Hospitality AI

• Hotel recommendation systems — combine ratings, tags, reviewer country, and hotel location for collaborative and content-based filtering
• Personalized travel AI — traveler type tags enable user segmentation for personalized hotel recommendations
• Review summarization — train abstractive summarization models to condense positive and negative feedback into concise hotel summaries
• Chatbot training for hospitality — use review text to build domain-specific conversational AI for hotel booking assistants

NLP & LLM Training

• LLM fine-tuning on review data — high-quality, real-world hospitality text for domain adaptation of language models
• Review generation models — train models to generate realistic hotel reviews given hotel attributes and traveler type
• Text classification — use tags and reviewer country as labels for multi-label classification tasks
• Semantic search — build dense retrieval systems over hotel reviews for question-answering applications

Business Intelligence & Analytics

• Seasonal sentiment analysis — use stayed_at and reviewed_at to identify how sentiment shifts by season or event
• Geo-targeted insights — map reviewer country vs hotel country to understand which traveler nationalities rate hotels most positively
• Review lag analysis — measure time between stay and review submission as a signal of satisfaction
• Competitive hotel intelligence — aggregate sentiment by hotel name, address, or country for market analysis

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Sample Data

{
  "hotel_name": "Grand Plaza Hotel",
  "hotel_address": "123 Main Street, Amsterdam",
  "country": "Netherlands",
  "review_title": "Great location, average breakfast",
  "reviewer_name": "Sarah M.",
  "reviewer_country": "United Kingdom",
  "positive_review_text": "The location was perfect — walking distance to all major attractions. Room was clean and staff were very friendly and helpful.",
  "negative_review_text": "Breakfast was disappointing for the price. Limited options and the coffee was lukewarm.",
  "stayed_at": "2024-11-10",
  "reviewed_at": "2024-11-15",
  "tags": "Couple, Leisure trip, Superior Double Room, Stayed 3 nights",
  "language": "en",
  "source": "Booking.com"
}

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Loading the Dataset

from datasets import load_dataset
import pandas as pd
 
dataset = load_dataset("crawlfeeds/Booking-Hotel-Reviews-Dataset")
df = dataset["train"].to_pandas()
 
# Reviews with both positive and negative text
dual_reviews = df[
    df["positive_review_text"].notna() & 
    df["negative_review_text"].notna()
]
 
# Filter by reviewer country
uk_reviewers = df[df["reviewer_country"] == "United Kingdom"]
 
# Filter by hotel country
netherlands_hotels = df[df["country"] == "Netherlands"]
 
# Create sentiment pairs for training
sentiment_pairs = df[["positive_review_text", "negative_review_text"]].dropna()
 
# Filter by traveler type using tags
solo_travelers = df[df["tags"].str.contains("Solo traveler", na=False)]
couples = df[df["tags"].str.contains("Couple", na=False)]
business = df[df["tags"].str.contains("Business trip", na=False)]
 
# Prepare text for LLM fine-tuning
text_data = df[["review_title", "positive_review_text", "negative_review_text"]].dropna()

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Data Quality Notes

• average_score and rating (0%) — numerical scores are available in the full commercial dataset at crawlfeeds.com
• helpful_count (0%) — available in the full dataset, useful for review quality and credibility modeling
• negative_review_text (35.9%) — partial coverage reflects real-world Booking.com behavior where many reviewers only leave positive feedback
• positive_review_text (55.8%) — similarly reflects organic reviewer behavior on the platform
• All 100% coverage fields are complete and production-ready in this sample

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Full Dataset & Custom Data

This is a 4,300 record sample. The full CrawlFeeds Booking.com dataset contains: - Tens of thousands of reviews across global hotel properties - Complete average_score and rating fields - Full helpful_count data - Hotels across 50+ countries - Weekly and monthly refresh options - Custom subsets by country, hotel rating, traveler type, or language Get the full dataset: crawlfeeds.com Request custom Booking.com data: crawlfeeds.com/contact

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License

This dataset is made available under CC BY-NC 4.0. Intended for research and non-commercial use. For commercial licensing, please contact crawlfeeds.com/contact.

Citation

@dataset{crawlfeeds_booking_reviews_2025,
  author    = {Crawl Feeds},
  title     = {Booking.com Hotel Reviews Dataset},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/crawlfeeds/Booking-Hotel-Reviews-Dataset}
}

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