Presentation 2014-06-21
Classifying Negative topics focusing on Sentiment Value of co-occurrence words
Koji WAJIMA, Tomomi OGAWA, Toshihiro FURUKAWA, Shigeru SHIMADA,
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Abstract(in English) This paper proposes the methods which classify the kind of topics as Negative topics. Many a Questions document has Negative fuzzy factors. In that case, the Sentiment Analysis based method cannot classify Questions document and Negative topics. Therefore, we propose a new classifying method. The proposed method can classify the topics under the conditions that many a document has Negative fuzzy factors. This paper specify Negative topics using Latent Dirichlet Allocation and Sentiment Analysis . The computational simulation shows the efficiency of the proposed method.
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Keyword(in English) Topic Model / Sentiment Analysis / Data Mining / Latent Dirichlet Allocation / Q&A site
Paper # DE2014-17
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Committee DE
Conference Date 2014/6/14(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Classifying Negative topics focusing on Sentiment Value of co-occurrence words
Sub Title (in English)
Keyword(1) Topic Model
Keyword(2) Sentiment Analysis
Keyword(3) Data Mining
Keyword(4) Latent Dirichlet Allocation
Keyword(5) Q&A site
1st Author's Name Koji WAJIMA
1st Author's Affiliation School of Industrial Technology, Advanced Institute of Industrial Technology()
2nd Author's Name Tomomi OGAWA
2nd Author's Affiliation Maebashi Institute of Technology
3rd Author's Name Toshihiro FURUKAWA
3rd Author's Affiliation Tokyo University of Science
4th Author's Name Shigeru SHIMADA
4th Author's Affiliation School of Industrial Technology, Advanced Institute of Industrial Technology
Date 2014-06-21
Paper # DE2014-17
Volume (vol) vol.114
Number (no) 101
Page pp.pp.-
#Pages 6
Date of Issue