The construction of Chinese microblog gender-specific thesauruses and user gender classification

Based on the statistical features, short text messages published by different gender users are different in terms of the words and semantics used. In this paper, two new features are constructed after constructing a gender-specific thesaurus. A new classification model is constructed by combining th...

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Detalles Bibliográficos
Autores principales: Zhu, Zhiliang, Ke, Zejun, Cui, Jiayin, Yu, Hai, Liu, Guoqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223889/
https://www.ncbi.nlm.nih.gov/pubmed/30465023
http://dx.doi.org/10.1007/s41109-018-0104-1
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author Zhu, Zhiliang
Ke, Zejun
Cui, Jiayin
Yu, Hai
Liu, Guoqi
author_facet Zhu, Zhiliang
Ke, Zejun
Cui, Jiayin
Yu, Hai
Liu, Guoqi
author_sort Zhu, Zhiliang
collection PubMed
description Based on the statistical features, short text messages published by different gender users are different in terms of the words and semantics used. In this paper, two new features are constructed after constructing a gender-specific thesaurus. A new classification model is constructed by combining the traditional statistical features and the improved text implicitness feature. The experimental evaluation performed on the Sina Weibo dataset demonstrated the effectiveness of gender-specific thesaurus-based features, and the improved text implicitness feature improved the accuracy of gender classification to 84.7%.
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spelling pubmed-62238892018-11-19 The construction of Chinese microblog gender-specific thesauruses and user gender classification Zhu, Zhiliang Ke, Zejun Cui, Jiayin Yu, Hai Liu, Guoqi Appl Netw Sci Research Based on the statistical features, short text messages published by different gender users are different in terms of the words and semantics used. In this paper, two new features are constructed after constructing a gender-specific thesaurus. A new classification model is constructed by combining the traditional statistical features and the improved text implicitness feature. The experimental evaluation performed on the Sina Weibo dataset demonstrated the effectiveness of gender-specific thesaurus-based features, and the improved text implicitness feature improved the accuracy of gender classification to 84.7%. Springer International Publishing 2018-11-08 2018 /pmc/articles/PMC6223889/ /pubmed/30465023 http://dx.doi.org/10.1007/s41109-018-0104-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Zhu, Zhiliang
Ke, Zejun
Cui, Jiayin
Yu, Hai
Liu, Guoqi
The construction of Chinese microblog gender-specific thesauruses and user gender classification
title The construction of Chinese microblog gender-specific thesauruses and user gender classification
title_full The construction of Chinese microblog gender-specific thesauruses and user gender classification
title_fullStr The construction of Chinese microblog gender-specific thesauruses and user gender classification
title_full_unstemmed The construction of Chinese microblog gender-specific thesauruses and user gender classification
title_short The construction of Chinese microblog gender-specific thesauruses and user gender classification
title_sort construction of chinese microblog gender-specific thesauruses and user gender classification
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223889/
https://www.ncbi.nlm.nih.gov/pubmed/30465023
http://dx.doi.org/10.1007/s41109-018-0104-1
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