Automating the Information Extraction from Semi-Structured Interview
  Transcripts
Angelina Parfenova
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.