The Use of Language Models in Automated Markup of Texts on Life Difficulties
Published: 2025, vol. 29, issue 3, pp. 53–75
Abstract
The paper addresses the laborious nature of manual coding of qualitative data in psychological studies that use content analysis. The effectiveness of automated text markup methods utilizing modern language models such as DeepSeek, GPT-4.1, and GPT-4.1-mini is assessed, and approaches to improve markup accuracy are developed. The work is based on descriptions of dificult life situations experienced by participants in a psychological study. The study confirms the practical feasibility of using language models as a tool that significantly reduces the time spent by researchers on the initial analysis of text data.
Keywords: ontent analysis, large language model, GPT-4.1, DeepSeek, dificult life situation, coping, situation perception.
BibTeX
@article{IS-Khlebnikova-Bityutskaya-Kalachev-Gasanov2025,
author = {Khlebnikova, Alena Andreevna and Bityutskaya, Ekaterina Vladislavovna and Kalachev, Gleb Vyacheslavovich and Gasanov, Elyar Eldarovich},
title = {{The Use of Language Models in Automated Markup of Texts on Life Difficulties}},
journal = {Intelligent Systems. Theory and Applications},
year = {2025},
volume = {29},
number = {3},
pages = {53--75},
}
AMSBIB
\Bibitem{IS-Khlebnikova-Bityutskaya-Kalachev-Gasanov2025}
\by A.\,A.~Khlebnikova, E.\,V.~Bityutskaya, G.\,V.~Kalachev, E.\,E.~Gasanov
\paper The Use of Language Models in Automated Markup of Texts on Life Difficulties
\jour Intelligent Systems. Theory and Applications
\yr 2025
\vol 29
\issue 3
\pages 53--75
\lang In Russian
Русский