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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Topic Model / Sentiment Analysis / Data Mining / Latent Dirichlet Allocation / Q&A site |
Paper # | DE2014-17 |
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Committee | DE |
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Conference Date | 2014/6/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Data Engineering (DE) |
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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 |
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