Identify Topic Relations in Scientific Literature Using Topic Modeling

Chen, H; Wang, X; Pan, S; Xiong, F

Identify Topic Relations in Scientific Literature Using Topic Modeling

Chen, H

Wang, X Pan, S

Xiong, F

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Engineering Management, 2019, PP, (99), pp. 1-13
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Chen, H https://orcid.org/0000-0002-0893-1817
dc.contributor.author	Wang, X
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Xiong, F
dc.date.accessioned	2020-09-18T22:23:14Z
dc.date.available	2020-09-18T22:23:14Z
dc.date.issued	2019-01-01
dc.identifier.citation	IEEE Transactions on Engineering Management, 2019, PP, (99), pp. 1-13
dc.identifier.issn	0018-9391
dc.identifier.issn	1558-0040
dc.identifier.uri	http://hdl.handle.net/10453/142748
dc.description.abstract	IEEE Over the past five years, topic models have been applied to bibliometrics research as an efficient tool for discovering latent and potentially useful content. The combination of topic modeling algorithms and bibliometrics has generated new challenges of interpreting and understanding the outcome of topic modeling. Motivated by these new challenges, this paper proposes a systematic methodology for topic analysis in scientific literature corpora to face the concerns of conducting post topic modeling analysis. By linking the corpus metadata with the discovered topics, we feature them with a number of topic-based analytic indices to explore their significance, developing trend, and received attention. A topic relation identification approach is then presented to quantitatively model the relations among the topics. To demonstrate the feasibility and effectiveness of our methodology, we present two case studies, using big data and dye-sensitized solar cell publications derived from searches in World of Science. Possible application of the methodology in telling good stories of a target corpus is also explored to facilitate further research management and opportunity discovery.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Engineering Management
dc.relation.isbasedon	10.1109/TEM.2019.2903115
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/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Business & Management
dc.title	Identify Topic Relations in Scientific Literature Using Topic Modeling
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	080109 Pattern Recognition and Data Mining
utslib.for	080705 Informetrics
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney
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/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2020-09-18T22:23:08Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

IEEE Over the past five years, topic models have been applied to bibliometrics research as an efficient tool for discovering latent and potentially useful content. The combination of topic modeling algorithms and bibliometrics has generated new challenges of interpreting and understanding the outcome of topic modeling. Motivated by these new challenges, this paper proposes a systematic methodology for topic analysis in scientific literature corpora to face the concerns of conducting post topic modeling analysis. By linking the corpus metadata with the discovered topics, we feature them with a number of topic-based analytic indices to explore their significance, developing trend, and received attention. A topic relation identification approach is then presented to quantitatively model the relations among the topics. To demonstrate the feasibility and effectiveness of our methodology, we present two case studies, using big data and dye-sensitized solar cell publications derived from searches in World of Science. Possible application of the methodology in telling good stories of a target corpus is also explored to facilitate further research management and opportunity discovery.

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http://hdl.handle.net/10453/142748