  We present a framework that allows users to incorporate the semantics of
their domain knowledge for topic model refinement while remaining
model-agnostic. Our approach enables users to (1) understand the semantic space
of the model, (2) identify regions of potential conflicts and problems, and (3)
readjust the semantic relation of concepts based on their understanding,
directly influencing the topic modeling. These tasks are supported by an
interactive visual analytics workspace that uses word-embedding projections to
define concept regions which can then be refined. The user-refined concepts are
independent of a particular document collection and can be transferred to
related corpora. All user interactions within the concept space directly affect
the semantic relations of the underlying vector space model, which, in turn,
change the topic modeling. In addition to direct manipulation, our system
guides the users' decision-making process through recommended interactions that
point out potential improvements. This targeted refinement aims at minimizing
the feedback required for an efficient human-in-the-loop process. We confirm
the improvements achieved through our approach in two user studies that show
topic model quality improvements through our visual knowledge externalization
and learning process.
