Cited by 175Computational and Text Analysis MethodsDiscourse Analysis in Language StudiesSentiment Analysis and Opinion Mining

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The utility of topic modelling for discourse studies: A critical evaluation

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Brookes, Gavin, McEnery, Tony·2019·Discourse Studies

DOI 10.1177/1461445618814032

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Abstract

This article explores and critically evaluates the potential contribution to discourse studies of topic modelling, a group of machine learning methods which have been used with the aim of automatically discovering thematic information in large collections of texts. We critically evaluate the utility of the thematic grouping of texts into ‘topics’ emerging from a large collection of online patient comments about the National Health Service (NHS) in England. We take two approaches to this, one inspired by methods adopted in existing topic modelling research and the other using more established methods of discourse analysis. In the study, we compare the insights produced by each approach and consider the extent to which the automatically generated topics might be of use to discourse analysts attempting to organise and study sizeable datasets. We found that the topic modelling approach was able to group texts into ‘topics’ that were truly thematically coherent with a mixed degree of success, while the more traditional approach to discourse analysis consistently provided a more nuanced perspective on the data which was ultimately closer to the ‘reality’ of the texts it contains. This study thus highlights issues concerning the use of topic modelling and offers recommendations and caveats to researchers employing such approaches to studying discourse in the future.

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Authors: Brookes, G., & McEnery, T.
Affiliations: Lancaster University (GB)
Corresponding author: Brookes, Gavin
ORCID: Brookes, Gavin
McEnery, Tony
Venue: Discourse Studies
Publisher: SAGE Publications
Published: 2018-12-21
Volume: 21
Issue: 1
Pages: 3-21
ISSN: 1461-4456 (ISSN-L)
1461-7080
DOI: 10.1177/1461445618814032
OpenAlex: W2883000515
Cited by: 175
References: 26
Citations by year: 2026: 22 · 2025: 32 · 2024: 25 · 2023: 28 · 2022: 32 · 2021: 20 · 2020: 10 · 2019: 5 · 2018: 1
FWCI: 33.30
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Discourse Studies (publishedVersion, cc-by-nc)
Funders: Economic and Social Research Council (ES/R008906/1, ES/K002155/1)
Topics: Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Social Sciences › Linguistics and Language › Discourse Analysis in Language Studies
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords: topic modeling, discourse analysis, National Health Service, thematic coherence, large text corpora

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The utility of topic modelling for discourse studies: A critical evaluation

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