Topic modeling is one of the more promising quantitative procedures
for exploring semantic structures. The creators of the corresponding algorithms use
large text sets to investigate hidden thematic connections which cannot be perceived
by the eye alone. We have, by contrast, tested a medium-sized corpus of novellas that
can be explored using both individual readings and statistical procedures We were
motivated by a previously little considered observation The scholars who have been
able to implement statistical procedures for literary corpus analysis in a pertinent
way were all extraordinarily familiar with their respective corpora Because we were
also very familiar with our set it was possible for us to order the topics being studied
according to text-relevant themes Using the keyword falcon topics we describe
another type of Topics ones which seem to reflect the special character of individual
texts.

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