BERTopic比LDA更适配社交媒体质性分析，生成更连贯可读的主题。
Moving Beyond LDA: A Comparison of Unsupervised Topic Modelling Techniques for Qualitative Data Analysis of Online Communities
- 用LLM构建主题模型，自动提取语义相关话题
- 8/12研究者偏好BERTopic，因主题更清晰有洞见
- 适合无编程基础的质性研究者快速洞察数据
社交媒体是质性研究的重要信息来源。尽管主题建模等计算方法能应对内容海量与多样问题，但研究者缺乏编程能力成为其应用的主要障碍。本文探讨基于大语言模型的BERTopic技术如何支持社交媒体的质性分析。我们通过访谈和实操评估，让12位质性研究者对比了LDA、NMF与BERTopic三种建模方法。结果表明，8位参与者更倾向使用BERTopic，因其能生成详细、连贯的主题簇，有助于深入理解并获取可行动的洞察。研究者普遍重视主题的相关性、逻辑结构及揭示数据中意外关联的能力。研究证实，基于LLM的主题建模技术具有支持质性分析的巨大潜力。
原文摘要 · Abstract (English)
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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