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

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

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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Research works printed but not linked: 5

  1. Blei D, 2009, Text Mining: Classification, Clustering, and Applications, 71
  2. Blei D, 2003, Journal of Machine Learning Research, 3, 993
  3. Jaworska S, 2016, Applied Linguistics, 39, 373
  4. Labov W, 1972, Language in the Inner City: Studies in the Black English Vernacular
  5. Mazarura J, 2014, Proceedings of the 2014 PRASA, RobMech and AfLaT International Joint Symposium

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Metadata

Authors
Brookes, G., & McEnery, T.
Affiliations
Lancaster University (GB)
Corresponding author
Brookes, Gavin
ORCID
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
Licence
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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