This paper explores the development and application of an automated system
designed to extract information from semi-structured interview transcripts.
Given the labor-intensive nature of traditional qualitative analysis methods,
such as coding, there exists a significant demand for tools that can facilitate
the analysis process. Our research investigates various topic modeling
techniques and concludes that the best model for analyzing interview texts is a
combination of BERT embeddings and HDBSCAN clustering. We present a
user-friendly software prototype that enables researchers, including those
without programming skills, to efficiently process and visualize the thematic
structure of interview data. This tool not only facilitates the initial stages
of qualitative analysis but also offers insights into the interconnectedness of
topics revealed, thereby enhancing the depth of qualitative analysis.

该研究论文探讨了一种自动化系统的开发和应用，该系统旨在从半结构化访谈文本中提取信息。研究发现，对于分析访谈文本，最佳模型是 BERT 嵌入和 HDBSCAN 聚类的组合。论文介绍了一个用户友好的软件原型，使研究人员能够高效处理和可视化访谈数据的主题结构。这个工具不仅有助于定性分析的初始阶段，还揭示了主题之间的相互关联，从而增强了定性分析的深度。