Lawrence Berkeley National Laboratory

Qualitative user research is a human-intensive approach that draws upon ethnographic methods from social sciences to develop insights about work practices to inform software design and development. Recent advances in data science, and in particular, natural language processing (NLP), enables the derivation of machine-generated insights to augment existing techniques. Our work describes our prototype framework based in Jupyter, a software tool that supports interactive data science and scientific computing, that leverages NLP techniques to make sense of transcribed texts from user interviews. This work also serves as a starting point for incorporating data science techniques in the qualitative analyses process.