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/

Enhance guest satisfaction with sentiment analysis of hotel reviews. Improve services and guest experiences effectively.

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 "Hotel Review Insights" dataset is a rich compilation of hotel reviews from various locations around the world. This dataset includes the following columns:

1. Index: A unique identifier for each review entry.
2. Name: The name of the hotel that was reviewed.
3. Area: The geographic area or city where the hotel is located.
4. Review Date: The date when the review was posted.
5. Rating Attribute: The aspect or attribute of the hotel being rated.
6. Rating (Out of 10): A numerical rating given by the reviewer for the specific attribute.
7. Review Text: The text of the review written by the guest.

This dataset provides valuable insights into guests' experiences and sentiments towards different aspects of hotels, helping researchers and analysts understand trends, preferences, and areas of improvement in the hospitality industry.

Data Science and Machine Learning Applications:

Sentiment Analysis: With the textual reviews and associated ratings, this dataset can be used to perform sentiment analysis, determining whether the reviews are positive, negative, or neutral. This can help hotels gauge customer satisfaction and identify areas for enhancement.

In just a few lines, the dataset empowers data scientists and machine learning practitioners to explore guest sentiments, study patterns, and build predictive models that contribute to enhancing guest experiences and the hospitality industry's overall quality.

This dataset contains 1,000 real hotel reviews scraped from TripAdvisor, including details such as review title, review text, rating, language, review date, hotel name, country, reviewer profile, user location, helpful votes, trip type, stay date, and management responses.

While this sample provides a ready-to-use subset for quick testing, researchers and enterprises can also request large-scale datasets with 100K to several million TripAdvisor reviews for advanced analytics, machine learning, and market research.

The data is multilingual (English, Spanish, German, French, Chinese, and more) and suitable for sentiment analysis, text classification, NLP training, recommendation systems, customer experience scoring, and travel industry benchmarking.

For bulk requests and tailored extractions, visit TripAdvisor Reviews Dataset.

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.

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.

🇬🇧 English summary 📊 Hotel Review Dataset — Spain (2019–2024) Includes 1,500 real guest reviews from hotels in Spain, with:

Hotel name City Date of review Full text of the review Rating (0 to 10) Sentiment classification (positive or negative)

✅ Format: CSV UTF-8-BOM (compatible with Excel, Python, Google Sheets)🔐 License: CC BY-NC 4.0 (non-commercial use)🎁 Free sample included Ideal for:

Sentiment analysis in Spanish NLP training and benchmarking TravelTech projects and AI experiments… See the full description on the dataset page: https://huggingface.co/datasets/Karpacious/hotel-reviews-es.

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.

11,600 hotel reviews by the average length of 74 words were selected.

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.

This dataset contains TripAdvisor guest reviews for major hotels in Salalah, Oman, collected through web scraping. It provides insights into guest satisfaction, sentiment, and ratings, making it a valuable resource for marketing, hospitality and tourism research, sentiment analysis, and tourism marketing studies.𝐇𝐨𝐭𝐞𝐥𝐬 𝐈𝐧𝐜𝐥𝐮𝐝𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭The dataset features guest reviews from the following hotels in Salalah:• Al Baleed Resort Salalah by Anantara• Belad Bont Resort• Crowne Plaza Resort Salalah• Fanar Hotel and Residences• Hilton Salalah Resort• Juweira Boutique Hotel• Millennium Resort Salalah• Salalah Gardens Hotel• Salalah Rotana Resort𝐓𝐢𝐦𝐞 𝐂𝐨𝐯𝐞𝐫𝐚𝐠𝐞The dataset captures all available guest reviews from the beginning of each hotel's presence on TripAdvisor up until February 2025.𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞 𝐭𝐨 𝐊𝐡𝐚𝐫𝐞𝐞𝐟 𝐓𝐨𝐮𝐫𝐢𝐬𝐦 𝐎𝐦𝐚𝐧 𝐕𝐢𝐬𝐢𝐨𝐧 2040This dataset is particularly beneficial for the following government agencies:• Ministry of Heritage and Tourism - Oman• Oman Chamber of Commerce & Industry (OCCI)• Dhofar Municipality and Dhofar Tourism Department• National Centre for Statistics and Information (NCSI)• Oman Vision 2040 Implementation Follow-up Unit• Ministry of Commerce, Industry, and Investment Promotion• Oman Tourism Development Company (OMRAN)• Ministry of Transport, Communications, and Information Technology (MTCIT)• Dhofar Governorate Office• Ministry of Environment and Climate AffairsIt also serves as a valuable resource for researchers, policymakers, and marketing, hospitality & tourism professionals to enhance Salalah’s tourism sector, improve guest satisfaction, and support Oman’s long-term vision for a thriving and sustainable tourism industry.Salalah experiences a surge in visitors during the Khareef season (monsoon season), a critical period for the hospitality industry. This dataset can help analyze guest experiences, identify service gaps, and optimize offerings during this peak tourism period.Oman Vision 2040 GoalsThe dataset aligns with Oman’s Vision 2040, which prioritizes tourism sector growth, economic diversification, and enhanced customer experiences. By leveraging sentiment analysis and guest insights, policymakers and hotel managers can develop data-driven strategies to improve hospitality services, attract more visitors, and enhance Salalah’s reputation as a premier travel destination.Potential Use CasesSentiment Analysis: Understanding guest satisfaction trends over timeTourism & Hospitality Research: Evaluating service quality and hotel performance across different yearsMarketing Insights: Identifying key drivers of positive and negative reviews for strategic decision-makingMachine Learning & NLP: Training models for text classification, sentiment prediction, and recommendation systems

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

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 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

Hotel Review Response Services Market Outlook

According to our latest research, the global hotel review response services market size reached USD 1.22 billion in 2024, and is poised to grow at a robust CAGR of 11.3% from 2025 to 2033. By the end of the forecast period, the market is expected to achieve a value of USD 3.17 billion. This remarkable growth is primarily driven by the escalating importance of online reputation management in the hospitality sector, as hotels increasingly recognize the direct correlation between guest feedback, review responses, and occupancy rates.

A key growth factor for the hotel review response services market is the rapidly evolving digital landscape, where online reviews have become a critical determinant of consumer choice in accommodation. With over 90% of travelers consulting online reviews before booking, hotels are under immense pressure to maintain a positive digital presence. This has led to a surge in demand for specialized review response services as hotels strive to engage professionally and promptly with guest feedback. The proliferation of review platforms such as TripAdvisor, Booking.com, and Google Reviews amplifies the need for consistent, high-quality responses that can influence booking decisions and enhance brand loyalty.

Another significant driver is the growing adoption of automation and artificial intelligence within the hospitality industry. Automated and hybrid response services are gaining traction as they enable hotels to manage a high volume of reviews efficiently, ensuring timely and personalized responses. These technologies not only streamline operations but also provide valuable insights through sentiment analysis and data analytics, empowering hotels to identify service gaps, monitor trends, and improve guest satisfaction. The integration of AI-driven solutions is particularly beneficial for large hotel chains and management companies dealing with reviews across multiple properties and platforms.

Additionally, the increasing emphasis on guest experience and personalized engagement is fueling market growth. Hotels are leveraging review response services not just for damage control, but as a strategic tool for building relationships and fostering repeat business. Effective response strategies can turn negative reviews into opportunities for service recovery, while positive interactions reinforce brand credibility. The trend towards outsourcing these services to specialized agencies or leveraging third-party platforms is also gaining momentum, as it allows hotels to focus on core operations while ensuring professional management of their online reputation.

From a regional perspective, North America currently dominates the hotel review response services market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The mature hospitality sector in North America, coupled with high internet penetration and a tech-savvy consumer base, has accelerated the adoption of review response services. Meanwhile, Asia Pacific is anticipated to witness the fastest growth rate during the forecast period, driven by the rapid expansion of the tourism industry, increasing digitalization, and the proliferation of midscale and budget hotels seeking to enhance their online visibility and guest engagement.

Service Type Analysis

The hotel review response services market is segmented by service type into manual response services, automated response services, and hybrid response services. Manual response services continue to hold a significant share, particularly among luxury and boutique hotels that prioritize personalized guest interaction and nuanced communication. These services involve trained professionals crafting tailored responses to each review, addressing specific guest concerns and highlighting unique aspects of the property. The human touch in manual responses is highly valued for its ability to convey empathy and authenticity, which are essential for building trust and loyalty among discerning travelers.

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.

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.