Estimating aspects in online reviews using topic model with 2-level learning

Takuya Konishi, Taro Tezuka, Fuminori Kimura, Akira Maeda

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

2 Citations (Scopus)

Abstract

In this paper, we propose a method for estimating latent aspects in online review documents. Review aspects represent features of items or services evaluated by users. We can expect to acquire useful features for users by discovering their review aspects. We apply topic models to this problem. Existing work proposed methods for estimating the topics of the whole document or sets of sentences with various window sizes. In this paper, we propose two-level learning approach that connects adjacent sentences when their topics are similar, and re-estimates topics once again using the determined processing units. In the experiments of precision using perplexity, we confirm our proposed method improves on the existing method.

Original languageEnglish
Title of host publicationInternational MultiConference of Engineers and Computer Scientists, IMECS 2012
PublisherNewswood Limited
Pages528-533
Number of pages6
ISBN (Print)9789881925114
Publication statusPublished - 2012
Externally publishedYes
Event2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, Hong Kong
Duration: Mar 14 2012Mar 16 2012

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2195
ISSN (Print)2078-0958

Other

Other2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012
CountryHong Kong
CityKowloon
Period3/14/123/16/12

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)

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