Coding Open-Ended Responses using Pseudo Response Generation by Large Language Models

AbstractSurvey research using open-ended responses is an important method thatcontributes to the discovery of unknown issues and new needs. However,survey research generally requires time and cost-consuming manual dataprocessing, indicating that it is difficult to analyze large dataset.To address this issue, we propose an LLM-based method to automate partsof the grounded theory approach (GTA), a representative approach of thequalitative data analysis. We generated and annotated pseudo open-endedresponses, and used them as the training data for the coding proceduresof GTA. Through evaluations, we showed that the models trained withpseudo open-ended responses are quite effective compared with thosetrained with manually annotated open-ended responses. We alsodemonstrate that the LLM-based approach is highly efficient andcost-saving compared to human-based approach.