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Qualitative research is an approach to understanding social phenomenon basedaround human interpretation of data, particularly text. Probabilistic topicmodelling is a machine learning approach that is also based around the analysisof text and often is used to in order to understand social phenomena. Both ofthese approaches aim to extract important themes or topics in a textual corpusand therefore we may see them as analogous to each other. However there arealso considerable differences in how the two approaches function. One is ahighly human interpretive process, the other is automated and statistical. Inthis paper we use this analogy as the basis for our Theme and Topic system, atool for qualitative researchers to conduct textual research that integratestopic modelling into an accessible interface. This is an example of a moregeneral approach to the design of interactive machine learning systems in whichexisting human professional processes can be used as the model for processesinvolving machine learning. This has the particular benefit of providing afamiliar approach to existing professionals, that may can make machine learningseem less alien and easier to learn. Our design approach has two elements. Wefirst investigate the steps professionals go through when performing tasks anddesign a workflow for Theme and Topic that integrates machine learning. We thendesigned interfaces for topic modelling in which familiar concepts fromqualitative research are mapped onto machine learning concepts. This makesthese the machine learning concepts more familiar and easier to learn forqualitative researchers.

Theme and Topic: How Qualitative Research and Topic Modeling Can Be Brought Together