Discovering Topic Representative Terms for Short Text Clustering

Yang, S; Huang, G; Cai, B

Discovering Topic Representative Terms for Short Text Clustering

Yang, S

Huang, G Cai, B

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7, pp. 92037-92047
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Yang, S https://orcid.org/0000-0002-6772-6805
dc.contributor.author	Huang, G
dc.contributor.author	Cai, B
dc.date.accessioned	2020-05-22T00:51:31Z
dc.date.available	2020-05-22T00:51:31Z
dc.date.issued	2019
dc.identifier.citation	IEEE Access, 2019, 7, pp. 92037-92047
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/140880
dc.description.abstract	© 2013 IEEE. Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo. However, the instant features (such as abbreviation and informal expression) and the limited length of short texts challenge the clustering task. Fortunately, short texts about the same topic often share some common terms (or term stems), which can effectively represent a topic (i.e., supported by a cluster of short texts), and we also call them topic representative terms. Taking advantage of topic representative terms, it is much easier to cluster short texts by grouping short texts into the most similar topic representative term groups. This paper provides a novel topic representative term discovery (TRTD) method for short text clustering. In our TRTD method, we discover groups of closely bound up topic representative terms by exploiting the closeness and significance of terms. The closeness of the topic representative terms is measured by their interdependent co-occurrence, and the significance is measured by their global term occurrences throughout the whole short text corpus. The experimental results on real-world datasets demonstrate that TRTD achieves better accuracy and efficiency in short text clustering than the state-of-the-art methods.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2019.2927345
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Discovering Topic Representative Terms for Short Text Clustering
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-22T00:51:26Z
pubs.publication-status	Published
pubs.volume	7
utslib.start-page	92037

Abstract:

© 2013 IEEE. Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo. However, the instant features (such as abbreviation and informal expression) and the limited length of short texts challenge the clustering task. Fortunately, short texts about the same topic often share some common terms (or term stems), which can effectively represent a topic (i.e., supported by a cluster of short texts), and we also call them topic representative terms. Taking advantage of topic representative terms, it is much easier to cluster short texts by grouping short texts into the most similar topic representative term groups. This paper provides a novel topic representative term discovery (TRTD) method for short text clustering. In our TRTD method, we discover groups of closely bound up topic representative terms by exploiting the closeness and significance of terms. The closeness of the topic representative terms is measured by their interdependent co-occurrence, and the significance is measured by their global term occurrences throughout the whole short text corpus. The experimental results on real-world datasets demonstrate that TRTD achieves better accuracy and efficiency in short text clustering than the state-of-the-art methods.

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http://hdl.handle.net/10453/140880