  Network-based procedures for topic detection in huge text collections offer
an intuitive alternative to probabilistic topic models. We present in detail a
method that is especially designed with the requirements of domain experts in
mind. Like similar methods, it employs community detection in term
co-occurrence graphs, but it is enhanced by including a resolution parameter
that can be used for changing the targeted topic granularity. We also establish
a term ranking and use semantic word-embedding for presenting term communities
in a way that facilitates their interpretation.
  We demonstrate the application of our method with a widely used corpus of
general news articles and show the results of detailed social-sciences expert
evaluations of detected topics at various resolutions. A comparison with topics
detected by Latent Dirichlet Allocation is also included. Finally, we discuss
factors that influence topic interpretation.
