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TripAdvisor datasets in Deepnote

By Filip Žitný

Updated on March 6, 2024

TripAdvisor remains a cornerstone for travelers seeking insights into hotels worldwide. Beyond its utility for travelers, the platform’s vast repository of reviews also serves as a goldmine for researchers delving into sentiment analysis, entity extraction, and more. Here, we explore four notable datasets extracted from TripAdvisor, each offering unique perspectives and challenges.

Four-City dataset


Reviews: 878,561

Hotels: 4,333

Format: JSON

This dataset provides a robust collection of hotel reviews across four cities, totalling nearly 1.3GB. Each review includes detailed ratings across various aspects such as cleanliness, service, and location, providing rich data for deep analysis.

Context and use:

Research Focus: Detection of fake hotel reviews.

Key Features: Ratings breakdown (overall, service, cleanliness, etc.), review text, author details, and more.

OpinRank dataset


Reviews: ~259,000

Cities: Dubai, Beijing, London, NYC, New Delhi, San Francisco, Shanghai, Montreal, Las Vegas, Chicago

Format: CSV (Tab-separated)

This dataset encompasses reviews from diverse global cities, emphasizing entity (feature) extraction and preference-based ranking. Despite lacking ratings, its focus on textual analysis offers valuable insights into user perceptions and preferences.

Context and use:

Research Focus: Entity extraction and ranking based on user preferences.

Key Features: Date, review title, full review text structured in a tab-separated format.

PrefLib dataset


Reviews: 675,069

Hotels: 1,851

Format: CSV (Comma-separated)

PrefLib integrates numerical aspect ratings with detailed review texts, providing a comprehensive view of user experiences. Each hotel’s reviews are spread across two files, enhancing the granularity of analysis for researchers.

Context and Use:

Research Focus: Varied, used in academic contexts.

Key Features: Aspect ratings (cleanliness, service, etc.), hotel and user identifiers, review text.

Latent aspect rating analysis dataset


Hotels: 1,851

Format: Semi-XML

This dataset includes hotel-specific XML files detailing user reviews along with aspect ratings ranging from 0 to 5 stars. It serves as a valuable resource for in-depth sentiment analysis and latent aspect identification.

Context and use:

Research Focus: Sentiment analysis, latent aspect identification.

Key Features: XML structure, aspect ratings (overall, cleanliness, etc.), detailed review content.

Insights and applications

Each dataset presents unique challenges and opportunities for researchers aiming to harness TripAdvisor’s wealth of user-generated content. From detecting fake reviews and entity extraction to sentiment analysis and latent aspect identification, these datasets cater to diverse research interests within the field of computational linguistics and data science.


TripAdvisor’s datasets not only facilitate travel decisions but also serve as pivotal resources for advancing research in sentiment analysis, entity extraction, and beyond. As these datasets continue to evolve, they promise even deeper insights into consumer preferences and hotel experiences worldwide. If you encounter any issues, please get in touch with our support. Happy analyzing in Deepnote!

Filip Žitný

Data Scientist

Follow Filip on Twitter, LinkedIn and GitHub

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