Presentation 2008-02-08
Discriminative topic-sentiment analysis from unsupervised topic corpora
Kugatsu SADAMITSU, Takahiro FUKUTOMI, Mikio YAMAMOTO,
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Abstract(in English) Topic information included in reviews can be beneficially used in sentiment analysis, and this paper proposes a method of performing sentiment analysis wherein topics are automatically differentiated even with review data without topic labels. Probabilistic Latent Semantic Analysis (PLSA) is used to model topic structure and then a model of sentiment words specific to the topic obtained so that accuracy of analysis is improved upon. When training the model, polarity posterior for each word are introduced to perform discriminative training in order that the topic information can be shared between polarities.
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Keyword(in English) Sentiment Analysis / topic models / PLSA / discriminative training / polarity posterior
Paper # NLC2007-97
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Committee NLC
Conference Date 2008/1/31(1days)
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Paper Information
Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Discriminative topic-sentiment analysis from unsupervised topic corpora
Sub Title (in English)
Keyword(1) Sentiment Analysis
Keyword(2) topic models
Keyword(3) PLSA
Keyword(4) discriminative training
Keyword(5) polarity posterior
1st Author's Name Kugatsu SADAMITSU
1st Author's Affiliation Graduate School of Systems and Information Engineering University of Tsukuba()
2nd Author's Name Takahiro FUKUTOMI
2nd Author's Affiliation Graduate School of Systems and Information Engineering University of Tsukuba
3rd Author's Name Mikio YAMAMOTO
3rd Author's Affiliation Graduate School of Systems and Information Engineering University of Tsukuba
Date 2008-02-08
Paper # NLC2007-97
Volume (vol) vol.107
Number (no) 480
Page pp.pp.-
#Pages 6
Date of Issue