  Topic models are a popular tool for clustering and analyzing textual data.
They allow texts to be classified on the basis of their affiliation to the
previously calculated topics. Despite their widespread use in research and
application, an in-depth analysis of topic models is still an open research
topic. State-of-the-art methods for interpreting topic models are based on
simple visualizations, such as similarity matrices, top-term lists or
embeddings, which are limited to a maximum of three dimensions. In this paper,
we propose an incidence-geometric method for deriving an ordinal structure from
flat topic models, such as non-negative matrix factorization. These enable the
analysis of the topic model in a higher (order) dimension and the possibility
of extracting conceptual relationships between several topics at once. Due to
the use of conceptual scaling, our approach does not introduce any artificial
topical relationships, such as artifacts of feature compression. Based on our
findings, we present a new visualization paradigm for concept hierarchies based
on ordinal motifs. These allow for a top-down view on topic spaces. We
introduce and demonstrate the applicability of our approach based on a topic
model derived from a corpus of scientific papers taken from 32 top machine
learning venues.
