Work

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

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Qualitative Methods

DOI: 10.1177/16094069251338898

Authors: Isil Anakok, Andrew Katz, Kai Jun Chew, Holly Matusovich

Institutions: Virginia Tech, Embry-Riddle Aeronautical University Daytona Beach Florida Campus, Embry–Riddle Aeronautical University

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,650

Funder: National Science Foundation