Generative models for mining latent aspects and their ratings from short reviews

Huayu Li, Rongcheng Lin, Richang Hong, Yong Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

A large number of online reviews have been accumulated on the Web, such as Amazon.com and Cnet.com. It is increasingly challenging to digest these reviews for both consumers and firms as the volume of reviews increases. A promising direction to ease such a burden is to automatically identify aspects of a product and reveal each individual's ratings on them from these reviews. The identified and rated aspects can help consumers understand the pros and cons of a product and make their purchase decisions, and help firms learn user feedbacks and improve their products and marketing strategy. While different methods have been introduced to tackle this problem in the past, few of them successfully model the intrinsic connection between aspect and aspect rating particularly in short reviews. To this end, in this paper, we first propose the Aspect Identification and Rating (AIR) model to model observed textual reviews and overall ratings in a generative way, where the sampled aspect rating influences the sampling of sentimental words on this aspect. Furthermore, we enhance AIR model to particularly address one unique characteristic of short reviews that aspects mentioned in reviews may be quite unbalanced, and develop another model namely AIRS. Within AIRS model, we allow an aspect to directly affect the sampling of a latent rating on this aspect in order to capture the mutual influence between aspect and aspect rating through the whole generative process. Finally, we examine our two models and compare them with other methods based on multiple real world data sets, including hotel reviews, beer reviews and app reviews. Experimental results clearly demonstrate the effectiveness and improvement of our models. Other potential applications driven by our results are also shown in the experiments.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages241-250
Number of pages10
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Externally publishedYes
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period11/14/1511/17/15

Keywords

  • Aspect Identification
  • Rating
  • Reviews

ASJC Scopus subject areas

  • Engineering(all)

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