Discovering Hidden Patterns: Applying Topic Modeling in Qualitative Research

Abstract

In qualitative studies, researchers must devote a significant amount of time and effort to extracting meaningful themes from huge sets of texts and examining the links between themes, which are frequently done manually. The availability of natural language models has enabled the application of a wide range of techniques for automatically detecting hierarchy, linkages, and latent themes in texts. This paper aims to investigate the coherence of the topics acquired from the analysis with the predefined themes, the hierarchy between the topics, the similarity between the topics and the proximity-distance between the topics by means of the topic model based on BERTopic using unstructured qualitative data. The qualitative data for this study was gathered from 106 students engaged in a university-run pedagogical formation certificate program. In BERTopic procedure, paraphrase-multilingual-MiniLM-L12-v2 model was used as sentence transformer model, UMAP was used as dimension reduction method and HDBSCAN algorithm was used as clustering method. It is found that BERTopic successfully identified six topics corresponding to the six predicted themes in unstructured texts. Moreover 74% of the texts containing some themes could be classified accurately. The algorithm was also able to successfully identify which topics were similar and which topics differed significantly from the others. It was concluded that BERTopic is a procedure that can identify themes that researchers do not notice depending on the density of the data in qualitative data analysis and has the potential to enable qualitative research to reach more detailed findings.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 3 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.