Abstract
Determining
the optimal number of topics to retain in the conduct of topic modeling (TM)
has received much attention over the last decade. Despite this work, issues
remain regarding the best methods to use for making such determinations. Approaches involving the use of relatively
simple statistics, most notably perplexity, have proven to be somewhat
inconsistent. Recently, researchers have suggested the use of change in
perplexity scores as a useful heuristic for determining the optimal number of
topics to retain. The current study builds on this earlier work by assessing
the utility of several methods borrowed from factor analysis and applied to
statistics commonly used in topic modeling, including perplexity and Alpha. These
new approaches are applied to several textual datasets and compared with more
traditional methods for determining the number of topics to retain. Results of these analyses demonstrate that
application of these methods borrowed from factor analysis does appear to be effective
for identifying the number of topics to retain.