The inspiration behind creating the OYO Review Dataset for sentiment analysis was to explore the sentiment and opinions expressed in hotel reviews on the OYO Hotels platform. Analyzing the sentiment of customer reviews can provide valuable insights into the overall satisfaction of guests, identify areas for improvement, and assist in making data-driven decisions to enhance the hotel experience. By collecting and curating this dataset, Deep Patel, Nikki Patel, and Nimil aimed to contribute to the field of sentiment analysis in the context of the hospitality industry. Sentiment analysis allows us to classify the sentiment expressed in textual data, such as reviews, into positive, negative, or neutral categories. This analysis can help hotel management and stakeholders understand customer sentiments, identify common patterns, and address concerns or issues that may affect the reputation and customer satisfaction of OYO Hotels. The dataset provides a valuable resource for training and evaluating sentiment analysis models specifically tailored to the hospitality domain. Researchers, data scientists, and practitioners can utilize this dataset to develop and test various machine learning and natural language processing techniques for sentiment analysis, such as classification algorithms, sentiment lexicons, or deep learning models. Overall, the goal of creating the OYO Review Dataset for sentiment analysis was to facilitate research and analysis in the area of customer sentiments and opinions in the hotel industry. By understanding the sentiment of hotel reviews, businesses can strive to improve their services, enhance customer satisfaction, and make data-driven decisions to elevate the overall guest experience.

Deep Patel: https://www.linkedin.com/in/deep-patel-55ab48199/ Nikki Patel: https://www.linkedin.com/in/nikipatel9/ Nimil lathiya: https://www.linkedin.com/in/nimil-lathiya-059a281b1/

Explore our extensive Booking Hotel Reviews Large Dataset, featuring over 20.8 million records of detailed customer feedback from hotels worldwide. Whether you're conducting sentiment analysis, market research, or competitive benchmarking, this dataset provides invaluable insights into customer experiences and preferences.

The dataset includes crucial information such as reviews, ratings, comments, and more, all sourced from travellers who booked through Booking.com. It's an ideal resource for businesses aiming to understand guest sentiments, improve service quality, or refine marketing strategies within the hospitality sector.

With this hotel reviews dataset, you can dive deep into trends and patterns that reveal what customers truly value during their stays. Whether you're analyzing reviews for sentiment analysis or studying traveller feedback from specific regions, this dataset delivers the insights you need.

Ready to get started? Download the complete hotel review dataset or connect with the Crawl Feeds team to request records tailored to specific countries or regions. Unlock the power of data and take your hospitality analysis to the next level!

Access 3 million+ US hotel reviews — submit your request today.

The TripAdvisor Vietnam Hotel Reviews Dataset is a comprehensive collection of user-generated reviews from the popular online travel platform TripAdvisor. This dataset offers valuable insights into the experiences, opinions, and ratings provided by individuals who have stayed at various hotels across Vietnam.

The dataset encompasses many hotels in different cities and regions of Vietnam, including popular tourist destinations such as Hanoi, Ho Chi Minh City, Da Nang, Nha Trang, and more. The reviews cover a diverse spectrum of accommodation types, ranging from budget guesthouses to luxurious resorts, providing a comprehensive representation of the Vietnamese hospitality industry.

Each review entry in the dataset includes a rich set of information, offering researchers, developers, and data analysts an in-depth understanding of hotel performance and customer satisfaction. Key attributes of the dataset include:

1. Review Text: The actual text of the review left by the user, which contains detailed descriptions, opinions, and feedback about their hotel experience.

2. Rating: The overall rating provided by the reviewer, typically ranging from 1 to 5 stars, reflects their satisfaction level with the hotel.

3. Date: The review was posted, enabling temporal analysis and tracking changes over time.

4. Location: The hotel's geographic location allows researchers to analyze regional variations in hotel performance and customer preferences.

The TripAdvisor Vietnam Hotel Reviews Dataset is valuable for various applications, including sentiment analysis, opinion mining, natural language processing, customer behavior analysis, recommender systems, and more. Researchers can leverage this dataset to gain deep insights into customer experiences, identify patterns, trends, and sentiments, and develop data-driven strategies for the Vietnamese hotel industry.

This dataset represents TripAdvisor Hotel Reviews about 20k tweets based on Sentiment Analysis rating from 1-5 basis.

Booking.com reviews dataset

Original source: https://www.kaggle.com/datasets/jiashenliu/515k-hotel-reviews-data-in-europe?resource=download&select=Hotel_Reviews.csv. This dataset subset has only 2 columns, with negative and positive review part, for sentiment analysis.

We have selected two most popular movie and hotel recommendation websites from those which attain a high rate in the Alexa website. We selected “beyazperde.com” and “otelpuan.com” for movie and hotel reviews, respectively. The reviews of 5,660 movies were investigated. The all 220,000 extracted reviews had been already rated by own authors using stars 1 to 5. As most of the reviews were positive, we selected the positive reviews as much as the negative ones to provide a balanced situation. The total of negative reviews rated by 1 or 2 stars were 26,700, thus, we randomly selected 26,700 out of 130,210 positive reviews rated by 4 or 5 stars. Overall, 53,400 movie reviews by the average length of 33 words were selected. The similar manner was used to hotel reviews with the difference that the hotel reviews had been rated by the numbers between 0 and 100 instead of stars. From 18,478 reviews extracted from 550 hotels, a balanced set of positive and negative reviews was selected. As there were only 5,802 negative hotel reviews using 0 to 40 rating, we selected 5800 out of 6499 positive reviews rated from 80 to 100. The average length of all 11,600 selected positive and negative hotel reviews were 74 which is more than two times of the movie reviews.

The USA Hotels Dataset from Booking.com is a rich collection of data related to hotels across the United States, extracted from Booking.com. This dataset includes essential information about hotel listings, such as hotel names, locations, prices, star ratings, customer reviews, and amenities offered. It's an ideal resource for researchers, data analysts, and businesses looking to explore the hospitality industry, analyze customer preferences, and understand pricing patterns in the U.S. hotel market.

Access 3 million+ US hotel reviews — submit your request today.

Key Features:

• Hotel Information: Includes hotel names, addresses, star ratings, and descriptions.
• Pricing Data: Nightly rates, discounts, and price variations by room type and season.
• Customer Reviews: Aggregated ratings and detailed user feedback from verified guests.
• Amenities: Detailed list of amenities provided by each hotel (e.g., Wi-Fi, parking, spa, swimming pool).
• Geographical Information: Hotel locations including city, state, and proximity to major landmarks.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to gauge hotel service quality and guest satisfaction.
• Price Analysis: Compare pricing across different hotels, locations, and time periods to identify trends.
• Recommendation Systems: Build recommendation engines based on customer ratings, reviews, and preferences.
• Tourism and Hospitality Research: Understand patterns in hotel demand and services across various U.S. cities.

Datasets of Tripadvisor reviews by UK residents of UK hotels and restaurants, together with the user's rating of the hotel.Datasets are split by:Hotel star level (2, 3, 4 or all[mixed]) or Restaurant;Reviewer gender (M=male-authored reviews; F=female-authored reviews; MF=equal numbers of male and female authored reviews for each rating level);Number of texts (1k, 2k, 4k, 8k, 16k, or all available)Each dataset contains equal numbers of reviews at each rating level.The reviews were selected at random from TripAdvisor.This data is from this paper:Thelwall, M. (2018). Gender bias in machine learning for sentiment analysis. Online Information Review, 42(3), 343-354. doi: 10.1108/OIR-05-2017-0152

The Booking.com Reviews Dataset is a comprehensive collection of user-generated reviews for hotels, hostels, bed & breakfasts, and other accommodations listed on Booking.com. This dataset provides detailed information on customer reviews, including ratings, review text, review dates, customer demographics, and more. It is a valuable resource for analyzing customer sentiment, service quality, and overall guest experiences across different types of accommodations worldwide.

Key Features:

• Review Data: Includes detailed customer reviews with both positive and negative feedback, providing insights into customer experiences and satisfaction levels.
• Ratings: Features individual ratings for various aspects of the accommodations, such as cleanliness, location, service, value for money, and overall satisfaction.
• Review Dates: Provides the dates of each review, enabling trend analysis over time.
• Accommodation Details: Includes information about the accommodations being reviewed, such as name and location.
• Language Support: Reviews are available in multiple languages, reflecting the diverse user base of Booking.com.

Use Cases:

• Sentiment Analysis: Ideal for businesses and researchers conducting sentiment analysis to understand customer opinions and trends in the hospitality industry.
• Market Research: Useful for market research and competitive analysis, identifying strengths and weaknesses of different accommodation types and regions.
• Machine Learning: Beneficial for developing machine learning models for natural language processing, sentiment classification, and recommendation systems.
• Customer Experience Improvement: Helps hotel managers and owners understand customer feedback to improve services and guest experiences.
• Academic Research: Suitable for academic research in hospitality management, consumer behavior, data science, and artificial intelligence.

Dataset Format:

The dataset is available in CSV format making it easy to use for data analysis, machine learning, and application development.

Access 3 million+ US hotel reviews — submit your request today.

This dataset can be used for the following applications and more:

** * Analyzing trends** Just as an example, you can see estimate how room occupancy must have been affected by the Covid 19 pandemic.

*** Sentiment Analysis / Opinion Mining** Using NLP techniques one can find out what the average user’s sentiment is towards each of the featured hotels in this dataset.

*** Topic / Aspect Extraction** Using categorization techniques one can quickly figure out how each of the hotels featured in this dataset fairs on attributes such as room quality, staff, food, check-in process, etc.

***Competitor Analysis** If you would like to find out what customers think about your competitors, a tailored dataset like the one featured in this blog post can enable you to do so with simple data analysis or visualization techniques.

Crawled over 2 weeks in January 2014, the Webis TripAdvisor Corpus 2014 (Webis-Tripad-14) consists of 266 061 reviews on 12 044 hotels by 208 785 users. Additionally, there is meta data about the hotels (such as location or overall ratings), the users (such as gender and age range) and the reviews itself (such as date posted and rating) available. We offer a download in json format: one file per hotel and one file containing all the user information.

The Webis TripAdvisor Corpus 2014 (Webis-Tripad-14) is designed in such a way that several different tasks can be performed on it, such as sentiment analysis, author profiling or usefulness detection.

The json-corpus consists of 12 045 files, where one of them contains all the user data and the others are one for each of the hotels in the data set. A detailed description of the data and the key/value pairs can be found as a README.txt in the download folder.

This comprehensive dataset offers a rich collection of over 5 million customer reviews for hotels and accommodations listed on Booking.com, specifically sourced from the United States. It provides invaluable insights into guest experiences, preferences, and sentiment across various properties and locations within the USA. This dataset is ideal for market research, sentiment analysis, hospitality trend identification, and building advanced recommendation systems.

Key Features:

• Geographic Focus: Exclusively reviews from properties located in the USA.
• Comprehensive Coverage: Includes a wide range of hotel types and sizes across different states and cities in the US, covering reviews from January 2020 to June 2025.
• Rich Detail: Each record provides detailed review information, allowing for in-depth analysis.
• Structured Format: Clean, organized, and ready for immediate use in various analytical tools and platforms.

Dive into a sample of 1,000+ records to experience the dataset's quality. For full access to this comprehensive data, submit your request at Booking reviews data.

Use Cases:

• Market Research: Gain insights into customer preferences and satisfaction in the US hospitality sector.
• Sentiment Analysis: Analyze the emotional tone of reviews to gauge customer sentiment towards hotels and services.
• Competitor Analysis: Benchmark hotel performance and identify areas for improvement against competitors.
• Trend Identification: Discover emerging trends in hotel amenities, service expectations, and guest behavior in the US.
• Recommendation Systems: Develop and train models to recommend hotels based on user preferences and review data.
• Natural Language Processing (NLP): Create and refine NLP models for text summarization, topic modeling, and opinion mining.
• Academic Research: Support studies on tourism, consumer behavior, and data science applications in hospitality.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

A simple Hotel Review Data useful for Text Analytics

The following is the data dictionary

REVIEW - The review submitted by customer who stayed in the hotel DATE - a simple dd-mm-yyyy format date when the review came Location Location from where the review came from

Unlock valuable insights with our comprehensive TripAdvisor Dataset, designed for businesses, analysts, and researchers to track customer reviews, ratings, and travel trends. This dataset provides structured and reliable data from TripAdvisor to enhance market research, competitive analysis, and customer satisfaction strategies.

Dataset Features

Business Listings: Access detailed information on hotels, restaurants, attractions, and other businesses, including names, locations, categories, and contact details. Customer Reviews & Ratings: Extract user-generated reviews, star ratings, review dates, and sentiment analysis to understand customer experiences and preferences. Pricing & Booking Data: Track pricing trends, availability, and booking options for hotels, flights, and travel services. Location & Geographical Insights: Analyze travel trends by region, city, or country to identify popular destinations and emerging markets.

Customizable Subsets for Specific Needs Our TripAdvisor Dataset is fully customizable, allowing you to filter data based on location, business type, review sentiment, or specific keywords. Whether you need broad coverage for industry analysis or focused data for customer insights, we tailor the dataset to your needs.

Popular Use Cases

Customer Satisfaction & Brand Monitoring: Track customer feedback, analyze sentiment, and improve service offerings based on real user reviews. Market Research & Competitive Analysis: Compare business performance, monitor competitor reviews, and identify industry trends. Travel & Hospitality Insights: Analyze travel patterns, popular destinations, and seasonal trends to optimize marketing strategies. AI & Machine Learning Applications: Use structured review data to train AI models for sentiment analysis, recommendation engines, and predictive analytics. Pricing Strategy & Revenue Optimization: Monitor pricing trends and customer demand to optimize pricing strategies for hotels, restaurants, and travel services.

Whether you're analyzing customer sentiment, tracking travel trends, or optimizing business strategies, our TripAdvisor Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

An English corpus for studying local sentiment flows and aspect-based sentiment analysis. It contains 2100 hotel reviews balanced with respect to the reviews’ sentiment scores. All reviews are segmented into subsentence-level statements that have then been manually classified as a fact, a positive, or a negative opinion. Also, all hotel aspects mentioned in the reviews have been annotated as such:arguana-tripadvisor-annotated-plus-software-v1.ziparguana-tripadvisor-annotated-v2.zipIn addition, we provide nearly 200k further hotel reviews without manual annotations:v1 upon requestarguana-tripadvisor-unannotated-v2.zip. The corpus is free-to-use for scientific purposes, not for commercial applications. In version 2, the annotated XMI files have been changed according to a new underlying type system that is more easily extendable. Notice that some adaptations of the software of version 1 are necessary to make it work with version 2.In case you publish any results related to the ArguAna TripAdvisor corpus, please cite our CICLing 2014 paper.

This dataset supports the study “Digital Information Systems in Hospitality: A Comparative Study of User Feedback and Rating Mechanisms Across Booking Websites.” It includes user review data collected from five booking platforms: Booking.com, TripAdvisor, Google Maps, Agoda, and Almosafer. The data were collected for hotels located in Riyadh that appear on all five platforms, using a crawler tool within the same time window to ensure consistency in hotel identity, location, and review timing. For each hotel, 25 user reviews were extracted, capturing the star rating, review content, and platform-specific details. The collected reviews were then analyzed using Azure Machine Learning Sentiment Analysis to classify them as positive, negative, or neutral. Azure’s model was used to ensure reliable sentiment classification based on pre-trained data from a wide range of products and services.

The emotion analysis of hotel online reviews is discussed by using the neural network model BERT, which proves that this method can not only help hotel network platforms fully understand customer needs but also help customers find suitable hotels according to their needs and affordability and help hotel recommendations be more intelligent. Therefore, using the pretraining BERT model, a number of emotion analytical experiments were carried out through fine-tuning, and a model with high classification accuracy was obtained by frequently adjusting the parameters during the experiment. The BERT layer was taken as a word vector layer, and the input text sequence was used as the input to the BERT layer for vector transformation. The output vectors of BERT passed through the corresponding neural network and were then classified by the softmax activation function. ERNIE is an enhancement of the BERT layer. Both models can lead to good classification results, but the latter performs better. ERNIE exhibits stronger classification and stability than BERT, which provides a promising research direction for the field of tourism and hotels.

The Webis Tripad 2013 Sentiment Corpus is a English text corpus of 2100 hotel reviews for the development and evaluation of approaches to sentiment flow analysis. Each document in this corpus is assigned an overall rating score, some metadata, and two kinds of annotations. First, each statement of a review's text has been classified with respect to its sentiment polarity (positive, negative, objective) by Amazon Mechanical Turk (AMT) workers. Second, hotel aspects mentioned in the texts were tagged by in-house domain experts.

To give an example, the sentence "The service was perfect and the rooms were clean." consists of two statements "The service was perfect" and "the rooms were clean", both with positive sentiment classification. The aspect in the first statement is "service" and "rooms" in the second, respectively.

The Agoda Hotel Listings Dataset provides a structured and detailed view of accommodations worldwide, offering essential data for travel industry professionals, market analysts, and businesses. This dataset includes key details such as hotel names, locations, review scores, pricing, availability, room configurations, amenities, guest reviews, property highlights, and property surroundings.

With this dataset, users can:

Analyze market trends to understand booking behaviors, pricing dynamics, and seasonal demand.
Enhance travel recommendations by identifying top-rated hotels based on reviews, location, and amenities.
Optimize pricing and revenue strategies by benchmarking property performance and availability patterns.
Assess guest satisfaction through sentiment analysis of ratings and reviews.
Refine location-based insights by analyzing property surroundings and nearby attractions.

Designed for hospitality businesses, travel platforms, AI-powered recommendation engines, and pricing strategists, this dataset enables data-driven decision-making to improve customer experience and business performance.

Use Cases

Agoda Hotel Listings in Thailand
Gain insights into Thailand’s hospitality market, from luxury resorts in Phuket to boutique hotels in Bangkok. Analyze review scores, availability trends, and traveler preferences to refine booking strategies.
Agoda Hotel Listings in Japan
Explore hotel data across Japan’s major cities and rural retreats, ideal for travel planners targeting visitors to Tokyo, Kyoto, and Osaka. This dataset includes review scores, pricing, and property highlights.
Agoda Hotel Listings with Review Scores Greater Than 9
A curated selection of high-rated hotels worldwide, ideal for luxury travel planners and market researchers focused on premium accommodations that consistently exceed guest expectations.
Agoda Hotel Listings in the United States with More Than 1000 Reviews
Analyze well-established and highly reviewed hotels across the U.S., ensuring reliable guest feedback for market insights and customer satisfaction benchmarking.

This dataset serves as an indispensable resource for travel analysts, hospitality businesses, and data-driven decision-makers, providing the intelligence needed to stay competitive in the ever-evolving travel industry.