Abstract:
Topic modeling is a type of text analysis that identifies clusters of co-occurring words, or latent topics. A challenging
step of topic modeling is determining the number of topics to extract. This tutorial describes tools researchers can use to
identify the number and labels of topics in topic modeling. First, we outline the procedure for narrowing down a large
range of models to a select number of candidate models. This procedure involves comparing the large set on fit metrics,
including exclusivity, residuals, variational lower bound, and semantic coherence. Next, we describe the comparison
of a small number of models using project goals as a guide and information about topic representative and solution
congruence. Finally, we describe tools for labeling topics, including frequent and exclusive words, key examples, and
correlations among topics.