  In the Humanities and Social Sciences, there is increasing interest in
approaches to information extraction, prediction, intelligent linkage, and
dimension reduction applicable to large text corpora. With approaches in these
fields being grounded in traditional statistical techniques, the need arises
for frameworks whereby advanced NLP techniques such as topic modelling may be
incorporated within classical methodologies. This paper provides a classical,
supervised, statistical learning framework for prediction from text, using
topic models as a data reduction method and the topics themselves as
predictors, alongside typical statistical tools for predictive modelling. We
apply this framework in a Social Sciences context (applied animal behaviour) as
well as a Humanities context (narrative analysis) as examples of this
framework. The results show that topic regression models perform comparably to
their much less efficient equivalents that use individual words as predictors.
