Interactive Topic Modeling for aiding qualitative content analysis

Bakharia, A; Bruza, P; Watters, J; Narayan, B; Sitbon, L

Interactive Topic Modeling for aiding qualitative content analysis

Bakharia, A Bruza, P Watters, J Narayan, B

Sitbon, L

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Publication Type:: Conference Proceeding
Citation:: CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval, 2016, pp. 213 - 222
Issue Date:: 2016-03-13

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Field	Value	Language
dc.contributor.author	Bakharia, A	en_US
dc.contributor.author	Bruza, P	en_US
dc.contributor.author	Watters, J	en_US
dc.contributor.author	Narayan, B https://orcid.org/0000-0001-8852-5589	en_US
dc.contributor.author	Sitbon, L	en_US
dc.date.issued	2016-03-13	en_US
dc.identifier.citation	CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval, 2016, pp. 213 - 222	en_US
dc.identifier.isbn	9781450337519	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43396
dc.description.abstract	© 2016 ACM. Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to inuence future algorithm development to better meet the requirements of qualitative content analysts.	en_US
dc.relation.ispartof	CHIIR 2016 - Proceedings of the 2016 ACM Conference on Human Information Interaction and Retrieval	en_US
dc.relation.isbasedon	10.1145/2854946.2854960	en_US
dc.title	Interactive Topic Modeling for aiding qualitative content analysis	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/e-Press
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/IKM and Digital Studies Program
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/School of Communication
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 ACM. Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to inuence future algorithm development to better meet the requirements of qualitative content analysts.

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http://hdl.handle.net/10453/43396