Automating Qualitative Data Analysis with Large Language Models

Angelina Parfenova, Alexander Denzler, Juergen Pfeffer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This PhD proposal aims to investigate ways of automating qualitative data analysis, specifically the thematic coding of texts. Despite existing methods vastly covered in literature, they mainly use Topic Modeling and other quantitative approaches which are far from resembling a human’s analysis outcome. This proposal examines the limitations of current research in the field. It proposes a novel methodology based on Large Language Models to tackle automated coding and make it as close as possible to the results of human researchers. This paper covers studies already done in this field and their limitations, existing software, the problem of duplicating the researcher bias, and the proposed methodology.

Original languageEnglish
Title of host publicationStudent Research Workshop
EditorsXiyan Fu, Eve Fleisig
PublisherAssociation for Computational Linguistics (ACL)
Pages177-185
Number of pages9
ISBN (Electronic)9798891760974
StatePublished - 2024
Event62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Bangkok, Thailand
Duration: 11 Aug 202416 Aug 2024

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
Volume4
ISSN (Print)0736-587X

Conference

Conference62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
Country/TerritoryThailand
CityBangkok
Period11/08/2416/08/24

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