Abstract:Statistical topic models are increasingly and popularly used by Digital Humanities scholars to perform distant reading tasks on literary data (Navarro-Colorado, 2018), (Hettinger et al., 2016. It allows us to estimate what people talk about. Especially Latent Dirichlet Allocation (LDA), see (Blei et al., 2003), 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,… Show more
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