The utility of topic modelling for discourse studies: a critical evaluation
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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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Existing technologies have limitations for conducting automated qualitative analysis within corpus approaches to discourse studies.
“recognising the limitations of existing technologies for conducting such analysis within a broader corpus linguistic methodology (Brookes & McEnery, 2019; Gillings et al., 2023)”
From Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT (2024) · cites this paper as Brookes & McEnery, 2019
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Roberts, S. G., Krykoniuk, K., Handford, M., Zhou, Y., Wu, J., & Chen, C. (2025) · Energy Research & Social Science · doi:10.1016/j.erss.2025.104275 · Cited by 1 · see citing works
- Authors
- Brookes, G., & McEnery, T.
- Affiliations
- Lancaster University (GB)
- Corresponding author
- Brookes, Gavin
- Venue
- Discourse Studies
- Publisher
- SAGE Publications
- Published
- 2018-12-21
- Volume
- 21
- Issue
- 1
- Pages
- 3-21
- ISSN
- 1461-4456 (ISSN-L)
- 1461-7080
- 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
- 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 › Arts and Humanities › Literature and Literary Theory › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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