Multi-aspect Reviews

Open Dataset
Creators:
Julian McAuley; Jure Leskovec; Dan Jurafsky
Publication Date:
2013
Data Category:
Dataset Description:
These datasets include reviews with multiple rated dimensions.It is particularly valuable for research in sentiment analysis, recommender systems, and user modeling, as it allows for a nuanced understanding of user opinions beyond overall ratings.​The most comprehensive of these are beer review datasets from Ratebeer and Beeradvocate, which include sensory aspects such as taste, look, feel, and smell. The data set is about 1 GB large.
Ratebeer:
  • Number of users: 40,213
  • Number of items: 110,419
  • Number of ratings/reviews: 2,855,232
  • Timespan: April, 2000 - November, 2011
BeerAdvocate:
  • Number of users: 33,387
  • Number of items: 66,051
  • Number of ratings/reviews: 1,586,259
  • Timespan: January, 1998 - November, 2011

The datasets are structured in a JSON format, with each entry representing a single review that includes:​

  • Product Information: Details about the beer being reviewed.​

  • User Information: Anonymized identifiers of the reviewers.​

  • Review Content: Textual feedback provided by the user.​

  • Ratings: Numerical scores for overall satisfaction and specific aspects (appearance, aroma, palate, taste).

Publications Citing This Dataset:
Schemmer, Max & Hemmer, Patrick & Nitsche, Maximilian & Kühl, Niklas & Vössing, Michael. (2022). A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making. 10.1145/3514094.3534128
Variables:
Details:
expertise machine learning
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