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Making open-ended questions attractive: leveraging topic modelling to evaluate survey responses
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Resumo(s)
While open-ended survey questions have declined due to time-intensive human analysis, ad vances in AI have renewed interest through the use of topic modeling. This thesis compares
Latent Dirichlet Allocation, Non-negative Matrix Factorization, Top2Vec, BERTopic, and
GPT-4.o in analyzing open-ended responses across two datasets (165, 170 answers). Model
outputs are evaluated for theme replication of manual analyses and the level of human review
required. Reviewed model outputs are also evaluated for theme replication supported by ex pert opinions. Results show that a human-in-the-loop approach with GPT-4.o requires mini mal manual review, closely replicates manual analysis, thus, effectively supports open-ended
question analysis for organizations.
Descrição
Palavras-chave
Natural language processing Topic modelling Thematic analysis GPT LDA NMF Top2Vec BERTopic Human-in-the-loop automated analysis