  Probabilistic topic modeling is a popular and powerful family of tools for
uncovering thematic structure in large sets of unstructured text documents.
While much attention has been directed towards the modeling algorithms and
their various extensions, comparatively few studies have concerned how to
present or visualize topic models in meaningful ways. In this paper, we present
a novel design that uses graphs to visually communicate topic structure and
meaning. By connecting topic nodes via descriptive keyterms, the graph
representation reveals topic similarities, topic meaning and shared, ambiguous
keyterms. At the same time, the graph can be used for information retrieval
purposes, to find documents by topic or topic subsets. To exemplify the utility
of the design, we illustrate its use for organizing and exploring corpora of
financial patents.
