SpecLDA: Modeling product reviews and specifications to generate augmented specifications

Dae Hoon Park, Chengxiang Zhai, Lifan Guo

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

Abstract

Product specifications are often available for a product on E-commerce websites. However, novice customers often do not have enough knowledge to understand all features of a product, especially advanced features. In order to provide useful knowledge to the customers, we propose to automatically generate augmented product specifications, which contains relevant opinions for product feature values, feature importance, and product-specific words. Specifically, we propose a novel Specification Latent Dirichlet Allocation (SpecLDA) that can enable us to effectively model product reviews and specifications at the same time. It mines review texts relevant to a feature value in order to inform customers what other customers have said about the feature value in reviews of the same product and also different products. SpecLDA can also infer importance of each feature and infer which words are special for each product so that customers quickly understand products. Experiment results show that SpecLDA can effectively model product reviews with specifications. The model can be used for any text collections with specification (key-value) type prior knowledge.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsJieping Ye, Suresh Venkatasubramanian
PublisherSociety for Industrial and Applied Mathematics Publications
Pages837-845
Number of pages9
ISBN (Electronic)9781510811522
StatePublished - Jan 1 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period4/30/155/2/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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