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| Presentation | 2017-09-08 10:50 Applicability of Structural Topic Model to job search site VOC text analysis -- Feature selection with Bayesian Network Structure Learning -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER)  NLC2017-25 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (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) |
| (in English) | Structural Topic Model / Bayesian net structure learning / feature selection / mutual information / sparseness / graphical model / visual text analysis / document clustering | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 207, NLC2017-25, pp. 53-58, Sept. 2017. | |
| Paper # | NLC2017-25 | |
| Date of Issue | 2017-08-31 (NLC) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NLC2017-25 |
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| Conference Information | |
| Committee | NLC |
| Conference Date | 2017-09-07 - 2017-09-08 |
| 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 |
| Paper Information | |
| Registration To | NLC |
| Conference Code | 2017-09-NLC |
| Language | Japanese |
| 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) |
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| Speaker | Author-1 |
| Date Time | 2017-09-08 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | NLC |
| Paper # | NLC2017-25 |
| Volume (vol) | vol.117 |
| Number (no) | no.207 |
| Page | pp.53-58 |
| #Pages | 6 |
| Date of Issue | 2017-08-31 (NLC) |
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