  Statistical topic models are increasingly and popularly used by Digital
Humanities scholars to perform distant reading tasks on literary data. It
allows us to estimate what people talk about. Especially Latent Dirichlet
Allocation (LDA) has shown its usefulness, as it is unsupervised, robust, easy
to use, scalable, and it offers interpretable results. In a preliminary study,
we apply LDA to a corpus of New High German poetry (textgrid, with 51k poems,
8m token), and use the distribution of topics over documents for a
classification of poems into time periods and for authorship attribution.
