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.

The total number of reviews and ratings on Tripadvisor worldwide has increased significantly since 2014, reaching the *********** mark in 2021. In the following years, the company mentioned that the number of reviews on the platform exceeded ***********. As of 2024, such reviews and ratings related to over **** million travel entries, including experiences, accommodation, restaurants, airlines, and cruises.

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.

Description

This dataset contains restaurant reviews from TripAdvisor for five European cities, capturing detailed information on users, restaurants (items), and reviews. It offers a comprehensive view of user experiences, opinions, and restaurant attributes.

Data Structure

User Information

• userId: Unique identifier for each user (hashed).
• name: Display name or username.
• location: User's location (city and country).

Restaurant Information (Items)

• itemId: Unique identifier for each restaurant.
• name: Restaurant name.
• city: City where the restaurant is located.
• priceInterval: Price range.
• url: Link to the restaurant’s TripAdvisor review page.
• rating: Average rating score for the restaurant.
• type: List of cuisine types (e.g., [Spanish, Mediterranean]).

Review Information

• reviewId: Unique identifier for each review.
• userId: Corresponding user who wrote the review.
• itemId: Restaurant associated with the review.
• title: Title of the review summarizing the user’s impression.
• text: Full text of the review describing the user’s experience.
• date: Date when the review was posted.
• rating: Numerical score (typically from 0 to 50, where 50 represents the highest satisfaction).
• language: Language of the review.
• images: List of URLs pointing to images uploaded by the user (if available).
• url: Link to the full review on TripAdvisor.

Code example

import pandas as pd

city = "Barcelona"
# Load restaurants
items = pd.read_pickle(f"{city}/items.pkl")
# Load users
users = pd.read_pickle(f"{city}/users.pkl")
# Load reviews
reviews = pd.read_pickle(f"{city}/reviews.pkl")

Abstract

Explore Hotel aspects and Predict the rating of each review.

About this dataset

Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged. With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!

How to use

• Predict Review Rating
• Topic Modeling on Reviews
• Explore key aspects that make hotels good or bad

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

Alam, M. H., Ryu, W.-J., Lee, S., 2016. Joint multi-grain topic sentiment: modeling semantic aspects for online reviews. Information Sciences 339, 206–223.

License

CC BY NC 4.0

Splash banner

Photo by Rhema Kallianpur on Unsplash.

Splash icon

Logo by Tripadvisor.

More Datasets

There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. If you use this data, please cite the following paper under submission process (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: parse_count: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) author_id: categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) restaurant_name: categorical (string), name of the restaurant matching the review rating_review: numerical (integer), review score in the range 1-5 sample: categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 review_id: categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) title_review: text, review title review_preview: text, preview of the review, truncated in the website when the text is very long review_full: text, complete review date: timestamp, publication date of the review in the format (day, month, year) city: categorical (string), city of the restaurant which the review was written for url_restaurant: text, restaurant url

Context

I love going to new restaurants and trying out their food and enjoying their ambience.

Content

This dataset contains information about the restaurant name, their location, the ratings, how many people have rated and also cuisine information.

Acknowledgements

I would like to thank TripAdvisor from where I scraped a little data to make a dataset of my own.

Context

Data was scraped from Tabelog. Whole scraping script is on my GitHub page.

Tabelog is a crowd-sourced restaurant-rating services. This site is the largest restaurant-review site which has 5.9 million reviews of 800,000 lists of restaurants Japan. You can’t only find reviews of restaurants but also you can refer to information of each restaurants and eating places in Japan.

Tabelog uses a 5-point scale like other websites such as Yelp and TripAdvisor do. However, unlike Yelp and TripAdvisor, good rating is between 3.00 ~ 4.00 points because users take their ratings seriously and many Michelin-star winners sit around 4.00.

Content

The dataset covers over 800 restaurant information in Kyoto Prefecture. The restaurants in the dataset were chosen out of all the restaurants listed on Tabelog based on their review numbers. Information of restaurants without any reviews by Tabelog users were deleted thus each restaurant in the dataset has, at least, over 1 review.

Acknowledgements

Data is from Tabelog.

Inspiration

Can you find the best restaurant in Kyoto?

Context

This corpus consists of truthful and deceptive hotel reviews of 20 Chicago hotels. The data is described in two papers according to the sentiment of the review. In particular, we discuss positive sentiment reviews in [1] and negative sentiment reviews in [2]. While we have tried to maintain consistent data preprocessing procedures across the data, there are differences which are explained in more detail in the associated papers. Please see those papers for specific details.

Content

This corpus contains:

• 400 truthful positive reviews from TripAdvisor (described in [1])
• 400 deceptive positive reviews from Mechanical Turk (described in [1])
• 400 truthful negative reviews from Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor and Yelp (described in [2])
• 400 deceptive negative reviews from Mechanical Turk (described in [2])

Each of the above datasets consist of 20 reviews for each of the 20 most popular Chicago hotels (see [1] for more details). The files are named according to the following conventions: Directories prefixed with fold correspond to a single fold from the cross-validation experiments reported in [1] and [2].

Hotels included in this dataset

• affinia: Affinia Chicago (now MileNorth, A Chicago Hotel)
• allegro: Hotel Allegro Chicago - a Kimpton Hotel
• amalfi: Amalfi Hotel Chicago
• ambassador: Ambassador East Hotel (now PUBLIC Chicago)
• conrad: Conrad Chicago
• fairmont: Fairmont Chicago Millennium Park
• hardrock: Hard Rock Hotel Chicago
• hilton: Hilton Chicago
• homewood: Homewood Suites by Hilton Chicago Downtown
• hyatt: Hyatt Regency Chicago
• intercontinental: InterContinental Chicago
• james: James Chicago
• knickerbocker: Millennium Knickerbocker Hotel Chicago
• monaco: Hotel Monaco Chicago - a Kimpton Hotel
• omni: Omni Chicago Hotel
• palmer: The Palmer House Hilton
• sheraton: Sheraton Chicago Hotel and Towers
• sofitel: Sofitel Chicago Water Tower
• swissotel: Swissotel Chicago
• talbott: The Talbott Hotel

References

[1] M. Ott, Y. Choi, C. Cardie, and J.T. Hancock. 2011. Finding Deceptive Opinion Spam by Any Stretch of the Imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.

[2] M. Ott, C. Cardie, and J.T. Hancock. 2013. Negative Deceptive Opinion Spam. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

Acknowledgements

If you use any of this data in your work, please cite the appropriate associated paper (described above). Please direct questions to Myle Ott (myleott@cs.cornell.edu).

Air travel is one of the most used ways of transit in our daily lives. So it's no wonder that more and more people are sharing their experiences with airlines and airports using web-based online surveys. This dataset aims to do topic modeling and sentiment analysis on Skytrax (airlinequality.com) and Tripadvisor (tripadvisor.com) postings where there is a lot of interest and engagement from people who have used it or want to use it for airlines.

Hi 👋, The food industry has grown rapidly. It produces a lot of restaurants each one of them has his one value wither it was in the type of food or the price and locations, as it became the target market for any new business, So why we don't collect data about these restaurants and TripAdvisor is the place to find all the information that we need.

Context

This dataset was scraped from TripAdvisor Tripadvisor, the world's largest travel platform, it contains all the information that helps the travelers around the world, to find the best accommodations, restaurants, experiences, airlines, and cruises, by reviewing all the information the traveler needs to know about starting from the name to the reviews of the previous customers. Here we focused only on restaurants in Saudi Arabia since improving tourism was a hot topic in the last period of time.

Content

This data contains information about restaurants in 3 main cities in Saudi Arabia: JEDDAH , RYADH, DAMMAM. Also, there is 4csv file 3 represents each city and the last one is the big one that contains all the 3. The information is : name | the name of the restaurant type | type of food that it represents location | the full location of the restaurant review score| how many points did he get review number| how many people give there feedback city| where is he opening hours | when he opens and when he close price range| start from - until out_of| his place out of the other restaurants represent the same type of food address_line1| extracted from location address_line2|extracted from location type 2 |extracted from type

Acknowledgements

This data is taken fro Trip-advisor website, and this project was required in order to graduate from GA data science Immersive course

Inspiration

There are a lot of things inspired me to do this one of them is restaurants and cafes are really important destinations when it comes to entertainment and also if you look at it from a business perspective it almost Succesful business if it was well planned. So, i thought about classifying this data to find the best location for a specific type of food in order to help any user or a new business to choose the perfect location. Or, you can combine these Data to do prediction or even recommendations. After all, Due to the current circumstances I really missed going out😢Maybe that was the main reason🙈.

This study aimed to explore and evaluate factors that impact the dining experience of vegetarian consumers within a range of vegetarian-friendly restaurants. To explore the factors and understand consumer experience, this study analyzed a vast number of user-generated contents of vegetarian consumers, which have become vital sources of consumer experience information. This study utilized machine-learning techniques and traditional methods to examine 54,299 TripAdvisor reviews of approximately 1,008 vegetarian-friendly restaurants in London. The study identified 21 topics that represent a holistic opinion influencing the dining experience of vegetarian customers. The results suggested that “value” is the most popular topic and had the highest topic percentage. The results of regression analyses revealed that five topics had a significant impact on restaurant ratings, while 12 topics had negative impacts. Restaurant managers who pay close attention to vegetarian aspects may utilize the findings of this study to satisfy vegetarian consumer requirements better and enhance service operations.

Seychelles Ecosystem Services, Other Nature Dependent Tourism:Mangrove Tourism At the global scale, the MOW team has developed a point-based map of mangrove tourism (Spalding and Parrett 2019). For the purpose of this project, the map was updated and improved with more recent and comprehensive TripAdvisor data and local-scale mangrove maps.The primary source of data for this analysis was provided by TripAdvisor, and consisted of locations of attractions and their associated reviews. We conducted a keyword search of all reviews (~65,000), and identified any that included the word “mangrove”. We then manually reviewed these reviews, removing any where the mangrove reference did not directly relate to the attraction location. Attractions that were directly associated with mangroves where then weighted by the number of reviews for that location as a proxy for their popularity. Model Outputs Mangrove Tourism Locations: Mangrove tourism attractions include locations and operators, based on sites listed in the popular travel web-site TripAdvisor. Some attractions, notably operators, show locations of headquarters rather than the actual mangrove destinations, which are typically nearby. Model Output Datasets Mangrove Tourism Locations Dataset name: Mangrove_Tourism_Locations.shpDataset type: ESRI shapefile, point featuresValues: Tourist attractions that are directly associated with mangroves were attributed with the number of reviews for that location, as a proxy for their popularity. Field ValuesPROPERTYNA Name of property associated with mangrove attractionPROPERTYLO Location of of property associated with mangrove attractionPLACE_TYPE Type of location (Accomodation, Attraction, Activity Provider)REGION_PRI Name of island where the attraction is locatedCITY_PRIMA Name of city where the attraction is locatedHOTEL_TYPE Type of accommodation associated with the attraction (Hotel, Resort, Other)Mangrv_Rev Number of TripAdvisor reviews mentioning mangrovesPA Protected area associated with the attraction References: Flickr User DataKlaus, R. (2015). Strengthening Seychelles ’ protected area system through NGO management modalities. Spalding, M., & Parrett, C. L. (2019). Global patterns in mangrove recreation and tourism. Marine Policy, 110, 103540.

TripAdvisor User Data

SubjQA is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a subjectivity label by annotators. Questions such as "How much does this product weigh?" is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity).

Results on variance differences of words with different POS across datasets.

Results on variance differences of words with different polarities across datasets.