Leveraging Generative Text Models and Natural Language Processing to Perform Traditional Thematic Data Analysis

Bibliographic Data

ID2411973
AuthorsIsil Anakok (0000-0002-1572-8024), Andrew Katz (0000-0002-3554-9015), Kai Jun Chew (0000-0001-7680-9808), Holly Matusovich (0000-0003-4335-6122)
Year2025
Volume24
Publication date2025-04-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Qualitative Methods (JOURNAL)
Journal identifiersISSN: 1609-4069 • E-ISSN: 1609-4069
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/16094069251338898
OpenAlexW4410549506
LanguageEN
Citations received5
References cited19

We explore the possibility of using natural language processing (NLP) and generative artificial intelligence (GAI) to streamline the process of thematic analysis (TA) for qualitative research. We followed traditional TA phases to demonstrate areas of alignment and discordance between (a) steps one might take with NLP and GAI and (b) traditional thematic analysis. Using a case study, we illustrate the application of this workflow to a real-world dataset. We start with processes involved in data analysis and translate those into analogous steps in a workflow that uses NLP and GAI. We then discuss the potential benefits and limitations of these NLP and GAI techniques, highlighting points of convergence and divergence with thematic analysis. Then, we highlight the importance of the central role of researchers during the process of NLP and GAI-assisted thematic analysis. Finally, we conclude with a discussion of the implications of this approach for qualitative research and suggestions for future work. Researchers who are interested in AI-assisted methods can benefit from the roadmap we provide in this study to understand the current landscape of NLP and GAI models for qualitative research

Cartography · Generative grammar · Generative model · Geography · Linguistics · Natural (archaeology · Natural language · Natural language processing · Thematic map · Advanced Text Analysis Techniques · Computational and Text Analysis Methods · Computer Science · Topic Modeling · Artificial Intelligence

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