  Induced by "big data," "topic modeling" has become an attractive alternative
to mapping co-words in terms of co-occurrences and co-absences using network
techniques. Does topic modeling provide an alternative for co-word mapping in
research practices using moderately sized document collections? We return to
the word/document matrix using first a single text with a strong argument ("The
Leiden Manifesto") and then upscale to a sample of moderate size (n = 687) to
study the pros and cons of the two approaches in terms of the resulting
possibilities for making semantic maps that can serve an argument. The results
from co-word mapping (using two different routines) versus topic modeling are
significantly uncorrelated. Whereas components in the co-word maps can easily
be designated, the topic models provide sets of words that are very differently
organized. In these samples, the topic models seem to reveal similarities other
than semantic ones (e.g., linguistic ones). In other words, topic modeling does
not replace co-word mapping in small and medium-sized sets; but the paper
leaves open the possibility that topic modeling would work well for the
semantic mapping of large sets.
