Diachronic Topics in New High German Poetry
This work provides a tool for digital humanities scholars to analyze large literary datasets, though it is incremental as it uses an existing method on new data.
The authors applied Latent Dirichlet Allocation (LDA) to a corpus of 51,000 New High German poems to classify poems by time period and authorship, demonstrating its utility in digital humanities for distant reading tasks.
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.