Year: 2020
Type: conference-paper
Abstract: Interview analysis is a technique employed in qualitative research. Researchers annotate (code) interview transcriptions, often with the help of Computer-Assisted Qualitative Data Analysis Software (CAQDAS). The tools available today largely replicate the manual process of annotation. In this article, we demonstrate how to use natural language processing (NLP) to increase the reproducibility and traceability of the process of applying codes to text data. We integrated an existing commercial machine--learning (ML) based concept extraction service into an NLP pipeline independent of domain specific rules. We applied our prototype in three qualitative studies to evaluate its capabilities of supporting researchers by providing recommendations consistent with their initial work. Unlike rule based approaches, our process can be applied to interviews from any domain, without additional burden to the researcher for creating a new ruleset. Our work using three example data sets shows that this approach shows promise for a real--life application, but further research is needed.
Authors: Andreas Kaufmann, Ann Barcomb, Dirk Riehle
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W3004597283