Moving Beyond LDA: A Comparison of Unsupervised Topic Modelling Techniques for Qualitative Data Analysis of Online Communities

• URL: http://arxiv.org/abs/2412.14486v1
• Date: Thu, 19 Dec 2024 03:19:18 GMT
• Title: Moving Beyond LDA: A Comparison of Unsupervised Topic Modelling Techniques for Qualitative Data Analysis of Online Communities
• Authors: Amandeep Kaur, James R. Wallace,
• Abstract summary: Social media constitutes a rich and influential source of information for qualitative researchers. computational techniques like topic modelling assist with managing the volume and diversity of social media content. We explore how BERTopic, an advanced Large Language Model (LLM)-based topic modelling technique, can support qualitative data analysis of social media.
• Score: 5.288687501686693
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• Abstract: Social media constitutes a rich and influential source of information for qualitative researchers. Although computational techniques like topic modelling assist with managing the volume and diversity of social media content, qualitative researcher's lack of programming expertise creates a significant barrier to their adoption. In this paper we explore how BERTopic, an advanced Large Language Model (LLM)-based topic modelling technique, can support qualitative data analysis of social media. We conducted interviews and hands-on evaluations in which qualitative researchers compared topics from three modelling techniques: LDA, NMF, and BERTopic. BERTopic was favoured by 8 of 12 participants for its ability to provide detailed, coherent clusters for deeper understanding and actionable insights. Participants also prioritised topic relevance, logical organisation, and the capacity to reveal unexpected relationships within the data. Our findings underscore the potential of LLM-based techniques for supporting qualitative analysis.

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