Bittm: A core biterms-based topic model for targeted analysis

Wang, J; Chen, L; Li, L; Wu, X

Bittm: A core biterms-based topic model for targeted analysis

Wang, J Chen, L

Li, L Wu, X

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2021, 11, (21), pp. 10162
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Li, L
dc.contributor.author	Wu, X
dc.date.accessioned	2022-05-18T04:48:08Z
dc.date.available	2022-05-18T04:48:08Z
dc.date.issued	2021-11-01
dc.identifier.citation	Applied Sciences (Switzerland), 2021, 11, (21), pp. 10162
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/157489
dc.description.abstract	While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.relation.isbasedon	10.3390/app112110162
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
dc.title	Bittm: A core biterms-based topic model for targeted analysis
dc.type	Journal Article
utslib.citation.volume	11
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-05-18T04:48:07Z
pubs.issue	21
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	21

Abstract:

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.

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http://hdl.handle.net/10453/157489