Introduction

TripAdvisor Statistics: TripAdvisor, which started its journey in the year 2000, has become an integral pillar in the travel industry, providing a space where travelers can write reviews, book hotels, and learn about experiences. With the year 2025 now arriving, the company continues to play an important role in the travel decisions made across the globe.

The article talks about how TripAdvisor statistics performed in the year 2024 through relevant figures regarding revenue, engagement from users, and market impact.​

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.

Like a lot of websites launched in the 2000s, the inspiration for TripAdvisor came to co-founder Stephen Kaufer after a frustrating experience. In his case, it was attempting to plan a family...

In June 2025, the number of visits to the travel and tourism website tripadvisor.com declined over the previous month, totaling roughly *** million. In 2025, tripadvisor.com was one of the most visited travel and tourism websites worldwide.

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.

Introduction

TripAdvisor Statistics: TripAdvisor is one of the world’s largest travel sites, and it will not change the way users travel. There’s a wealth of reviews and, hence, millions of users on the site. They will use the information learned from these reviews.

TripAdvisor, founded in February 2000 and headquartered in Needham, Massachusetts, operates worldwide with its flagship site available in approximately 40 countries and 20 languages. As of 2023, it hosts nearly 1 billion user-generated reviews and opinions across about 8 million listings—including hotels, restaurants, attractions, and more. In 2023, its platform attracted around 294 million unique visitors across the website and app, generating total revenue of US$ 1.788 billion in that year.

The company employed 2,845 staff in 2023. In 2024 alone, its community contributed 79.7 million new submissions, comprising 31.1 million reviews and 38.1 million other contributions. However, in 2024, TripAdvisor also removed 2.7 million fraudulent reviews, equivalent to nearly one in twelve submissions.

hence, both travelers and companies that offer travel services will benefit from them. Below is a comprehensive report on TripAdvisor statistics for 2024.

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

1) Data Introduction • The TripAdvisor Hotels Dataset is a travel and lodging analysis dataset that collects information about hotels around the world in a tabular format, including locations, ratings, number of reviews, price points, amenities, room types, and user reviews.

2) Data Utilization (1) TripAdvisor Hotels Dataset has characteristics that: • Each row contains a variety of attributes needed to choose and evaluate accommodation, including hotel name, address (including latitude and longitude), rating, number of reviews, price range, room and amenities information, user reviews, language, and nearby attractions. • Data is available in various formats such as JSON and CSV, and includes detailed reviews, rating distribution, and service details (clean, location, service, etc.) by hotel for multi-faceted analysis. (2) TripAdvisor Hotels Dataset can be used to: • Analysis of hotel ratings and reviews: Various information such as ratings, review texts, amenities, etc. can be used to assess hotel service quality, analyze user satisfaction, and calculate popular hotel rankings. • Traveler's Customized Recommendation and Marketing Strategy: Based on data such as location, price, and review pattern, it can be applied to developing a customized hotel recommendation system, establishing a regional marketing strategy, and analyzing competitors.

Dataset

This dataset was created by Amine Elyazidi

Released under CC0: Public Domain

Contents

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")

TripAdvisor hotel review dataset, hotel links and hotel data. The user reviews and hotel basic information data from the hotel introduction pages of the three important cities of Shanghai, London and New York in the TripAdvisor hotel section are crawled in order of ranking, including more than 60,000 reviews from more than 100 hotels.

Data collected from Tripadvisor during the EU-funded INCULTUM project. The collected variables are presented and summarised, and the data is validated though a simple comparison with official statistics. ATTRACTIONS data module (attr.csv): Consists of a list of all tourist attractions listed on the respective country's Things to do page on Tripadvisor at the time of data scraping. REVIEWS data module (reviews_XX.csv): Consists of reviews in different "XX" languages for each respective attraction. USERS data module (users.csv): Contains basic information on the users who wrote at least one review for at least one attraction in our sample of countries. TRAVEL HISTORY data module (travelHistory.csv): Contains data on reviews written by users included in the user profile module. For further details, refer to the Data Manual and Description.

TripAdvisor reported $65M in Cost of Sales for its fiscal quarter ending in June of 2025. Data for TripAdvisor | TRIP - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last August in 2025.

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

This statistic shows the number of listings on Tripadvisor worldwide from 2014 to 2019, by type. In 2019, there were *** million restaurants listed on Tripadvisor.

Tripadvisor is a travel website that helps customers in gathering travel information and posting reviews and opinions. Tripadvisor operates websites in ** countries and ** languages.

Hotel Review Data scraped from TripAdvisor for the most populous city in each state.

Raw data available as csv in the main folder, semi-processed in the Cleaned folder.

Contains TripAdvisor and Yelp review data, and tweets related to points of interest in Florida and New York. twitter, yelp, Florida, New York, data mining

Traffic analytics, rankings, and competitive metrics for tripadvisor.com as of June 2025

Tourism industries have the potential to contribute to the country's income, and as they should, we expect this industry to continue to grow each year. Indonesia is one of the well-known countries with incredible destinations to visit by domestic and international tourists that are continuously growing. There are many ways to determine a suitable strategy to understand tourist behavior, such as understanding tourist mobility, sentiment, and problems. Using tourist reviews or user-generated content (UGC) data on the Tripadvisor website, we employ social network analysis (SNA) to identify tourist mobility, favorite and in-between destination using network metrics and measurements. We use sentiment analysis to classify tourist sentiment. And multiclass text classification method to find out various problems in tourist reviews. We also construct a text corpus for the tourism domain to classify tourism problems. The results represent the complex tourist mobility to recognize the favorite destination, the tourist sentiment in each destination, and the problem in Bali tourism. The combined model benefits many stakeholders such as tourists, the government, and business organizations.

Abstract Justified purpose of the topic: analysis of catering services has been the object of several studies in the field of tourism, and in this work, we analyze the positioning of the thirteen starred restaurants in the Michelin Guide 2016 in the city of. Objective: Within a marketing and management vision, the present research aims to analyze the positioning of restaurants through the evaluations posted on said social media of said restaurants. Methodology and approach: The study is characterized by being exploratory and descriptive, with a quantitative approach, performed through the collection of user-generated content (CGU) or User-Generated Content, on the site related to 1,300 customer ratings of restaurants that form the research universes and as an analysis tool Iramuteq software was used. Results: The results obtained through the descending hierarchical classification (CHD) that IRAMUTEQ retained and divided the total of the corpus of the evaluations collected into four classes: 1) attendance; 2) the restaurants; 3) hospitalitythe service; 4) food, characterizing what customers perceive to be important when choosing and utilizing catering projects and, through the analysis, point out that high-class restaurants in São Paulo are ranked by attribute, by users, by category of products and by class of service. Original document: This work is original on the theme and framework.