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Induced by "big data," "topic modeling" has become an attractive alternativeto mapping co-words in terms of co-occurrences and co-absences using networktechniques. Does topic modeling provide an alternative for co-word mapping inresearch practices using moderately sized document collections? We return tothe word/document matrix using first a single text with a strong argument ("TheLeiden Manifesto") and then upscale to a sample of moderate size (n = 687) tostudy the pros and cons of the two approaches in terms of the resultingpossibilities for making semantic maps that can serve an argument. The resultsfrom co-word mapping (using two different routines) versus topic modeling aresignificantly uncorrelated. Whereas components in the co-word maps can easilybe designated, the topic models provide sets of words that are very differentlyorganized. In these samples, the topic models seem to reveal similarities otherthan semantic ones (e.g., linguistic ones). In other words, topic modeling doesnot replace co-word mapping in small and medium-sized sets; but the paperleaves open the possibility that topic modeling would work well for thesemantic mapping of large sets.

Co-word Maps and Topic Modeling: A Comparison Using Small and Medium-Sized Corpora (n < 1000)