  Qualitative research is an approach to understanding social phenomenon based
around human interpretation of data, particularly text. Probabilistic topic
modelling is a machine learning approach that is also based around the analysis
of text and often is used to in order to understand social phenomena. Both of
these approaches aim to extract important themes or topics in a textual corpus
and therefore we may see them as analogous to each other. However there are
also considerable differences in how the two approaches function. One is a
highly human interpretive process, the other is automated and statistical. In
this paper we use this analogy as the basis for our Theme and Topic system, a
tool for qualitative researchers to conduct textual research that integrates
topic modelling into an accessible interface. This is an example of a more
general approach to the design of interactive machine learning systems in which
existing human professional processes can be used as the model for processes
involving machine learning. This has the particular benefit of providing a
familiar approach to existing professionals, that may can make machine learning
seem less alien and easier to learn. Our design approach has two elements. We
first investigate the steps professionals go through when performing tasks and
design a workflow for Theme and Topic that integrates machine learning. We then
designed interfaces for topic modelling in which familiar concepts from
qualitative research are mapped onto machine learning concepts. This makes
these the machine learning concepts more familiar and easier to learn for
qualitative researchers.
