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Multi-aspect Reviews

Multi-aspect Reviews

Stefan
Open Dataset
Creators: Julian McAuley; Jure Leskovec; Dan Jurafsky
Publication Date: 2013
Creators: Julian McAuley; Jure Leskovec; Dan Jurafsky
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).

Web data: Amazon movie reviews

Web data: Amazon movie reviews

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset is a collection of approximately 8 million movie reviews from Amazon, spanning over a decade up to October 2012. It is particularly valuable for analyzing consumer behavior, sentiment analysis, and the evolution of user expertise in online reviews. In total, the dataset has a size of 3,1 GB. Each review includes detailed information such as the product’s unique identifier (ASIN), user ID, profile name, helpfulness rating, score, time of review (in Unix time), summary, and the full text of the review.

The dataset is organized with each review capturing multiple attributes:​

  • Product Information: Including the product’s unique identifier (ASIN).​

  • User Information: Such as user ID and profile name.​

  • Review Details: Encompassing helpfulness rating, score, time of review, summary, and the full text.

 

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