| Presentation | 2017-09-08 Applicability of Structural Topic Model to job search site VOC text analysis Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We describe the result of examination applying Structural Topic Model and Bayesian net structure learning complementarily to text analysis of "user / withdrawal questionnaire" of job change site DODA operated by PERSOL CAREER. It is effective to visualize and analyze the relevance of topic correlation, topic ~ metadata covariates obtained by Structure Topic Model which is rich expressive topic by feature selection by Bayesian net structure learning. Furthermore, the topic allocation "document - topic" matrix of the document obtained by the Structure Topic Model is also a document vector suitable for document clustering because it is information dimension compression of the "document - word" matrix. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Structural Topic Model / Bayesian net structure learning / feature selection / mutual information / sparseness / graphical model / visual text analysis / document clustering |
| Paper # | NLC2017-25 |
| Date of Issue | 2017-08-31 (NLC) |
| Conference Information | |
| Committee | NLC |
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| Conference Date | 2017/9/7(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Seikei University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The Eleventh Text Analytics Symposium |
| Chair | Hiroshi Kanayama(IBM) |
| Vice Chair | Takeshi Sakaki(Hottolink) / Kazutaka Shimada(Kyushu Inst. of Tech.) |
| Secretary | Takeshi Sakaki(Ryukoku Univ.) / Kazutaka Shimada(NTT) |
| Assistant | Mitsuo Yoshida(Toyohashi Univ. of Tech.) / Takeshi Kobayakawa(NICT) |
| Paper Information | |
| Registration To | Technical Committee on Natural Language Understanding and Models of Communication |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Applicability of Structural Topic Model to job search site VOC text analysis |
| Sub Title (in English) | Feature selection with Bayesian Network Structure Learning |
| Keyword(1) | Structural Topic Model |
| Keyword(2) | Bayesian net structure learning |
| Keyword(3) | feature selection |
| Keyword(4) | mutual information |
| Keyword(5) | sparseness |
| Keyword(6) | graphical model |
| Keyword(7) | visual text analysis |
| Keyword(8) | document clustering |
| 1st Author's Name | Norimitsu Kubono |
| 1st Author's Affiliation | PERSOL CAREER(PERSOL CAREER) |
| 2nd Author's Name | Nozomi Hiyoshi |
| 2nd Author's Affiliation | PERSOL CAREER(PERSOL CAREER) |
| 3rd Author's Name | Daiju Akashi |
| 3rd Author's Affiliation | PERSOL CAREER(PERSOL CAREER) |
| Date | 2017-09-08 |
| Paper # | NLC2017-25 |
| Volume (vol) | vol.117 |
| Number (no) | NLC-207 |
| Page | pp.pp.53-58(NLC), |
| #Pages | 6 |
| Date of Issue | 2017-08-31 (NLC) |