Automating the Information Extraction from Semi-Structured Interview Transcripts
This tool addresses the demand for facilitating qualitative analysis for researchers, including those without programming skills, though it is incremental as it builds on existing topic modeling techniques.
The paper tackled the problem of labor-intensive qualitative analysis by developing an automated system that extracts information from semi-structured interview transcripts, resulting in a user-friendly software prototype that enables efficient processing and visualization of thematic structures.
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.