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

Takuya Konishi, Taro Tezuka, Fuminori Kimura, Akira Maeda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルInternational MultiConference of Engineers and Computer Scientists, IMECS 2012
出版社Newswood Limited
ページ528-533
ページ数6
ISBN(印刷版)9789881925114
出版ステータス出版済み - 2012
外部発表はい
イベント2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012 - Kowloon, 香港
継続期間: 3 14 20123 16 2012

出版物シリーズ

名前Lecture Notes in Engineering and Computer Science
2195
ISSN(印刷版)2078-0958

その他

その他2012 International MultiConference of Engineers and Computer Scientists, IMECS 2012
国/地域香港
CityKowloon
Period3/14/123/16/12

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(その他)

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