User Review Datasets

By / AI Implementation, Dataset

If you are looking for user review data sets for opinion analysis / sentiment analysis tasks, there are quite a few out there. These dataset below contain reviews from Rotten Tomatoes, Amazon, TripAdvisor, Yelp, Edmunds.com and so on.Here are some of the many dataset available out there:

Dataset Domain Description Courtesy Of
Movie Reviews Data Set Movies This is a collection of movie reviews used for various opinion analysis tasks;  You would find reviews split into positive and negative classes as well as reviews split into subjective and objective sentences.

This dataset was initially used to predict polarity ratings (+ve/-ve).

 Pang & Lee
Multi-Domain Sentiment Dataset Products (books, dvds..) Product reviews  from Amazon.com covering various product types (such as books, dvds, musical instruments). The data has been split into positive and negative reviews. There are more than  100,000 reviews in this dataset. The reviews come with corresponding rating stars.

This dataset was initially used to predict polarity ratings (+ve/-ve).

 Blitzer et. al
LARA Review Dataset Hotels & Products Reviews from Amazon.com and TripAdvisor. It contains attributes such as author name, content, date and the ratings.

This dataset was initially used to decompose user reviews to preference rating on aspects.

 Wang et. al
Opinosis Review Dataset Hotels, Cars, Electronics Topic related sentences extracted from user reviews. You will find 51 topics with  approximately 100 sentences each (on average). The reviews were obtained from multiple sources – Tripadvisor (hotels), Edmunds.com (cars) and Amazon.com (various electronics).

This dataset was used for text summarization of opinions.

 Ganesan et. al
OpinRank Tripadvisor and Edmunds.com Dataset  Hotels & Cars Reviews of cars and and hotels collected from Tripadvisor (~259,000 reviews) and Edmunds (~42,230 reviews). For cars, the extracted fields include dates, author names, favorites and the full textual review. For hotels, the fields include date, review title and the full review and also includes gold standard judgments for ranking.

This dataset was initially used for opinion-based entity ranking.

 Ganesan & Zhai
Restaurant Review Dataset Restaurants Contains a total 52077 reviews. The fields contain rating information, review counts, percent and cuisine type Elhadad
SNAP Review Dataset Products Contains a 34,686,770 Amazon user reviews from 6,643,669 users.

This dataset was initially used for recommendation systems.

 McAuley
MovieLens Dataset Movies
  • 100,000 ratings (1-5) from 943 users on 1682 movies.
  • Each user has rated at least 20 movies.
  • Simple demographic info for the users (age, gender, occupation, zip)
Please note that the review text is not available
 GroupLens Research Project at the University of Minnesota.
Micropinion Generation Dataset (CNET) Electronics 330 review texts. The reviews are on products from various categories like tv, cell phones, gps etc.
This dataset was used for text summarization of opinions.		 Ganesan & Zhai

About The Author

Kavita Ganesan is the author of the Amazon bestselling book, The Business Case for AI and founder of Opinosis Analytics, an AI consulting company providing advisory, strategy and development services. With over 15 years of experience in the field, Kavita has scaled and delivered multiple successful AI initiatives for large companies such as eBay, 3M, and GitHub, as well as smaller organizations. Kavita holds advanced degrees from prestigious computer science programs, specifically a Masters's from the University of Southern California and a Ph.D. from the University of Illinois at Urbana Champaign, specializing in NLP, Search Technologies, and Machine Learning. Kavita has been featured by numerous media outlets, including Forbes, CEOWorld, CMSWire, Verizon, SDTimes, Techopedia, and Ted Magazine.

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