<head xmlns="http://www.tei-c.org/ns/1.0">Topic features</head> <p xmlns="http://www.tei-c.org/ns/1.0">We use Latent Dirichlet Allocation (LDA) by Blei et al. (2003) to extract topics from our data. In literary texts topics sometimes represent themes, but more often they represent topoi, often used ways of telling a story or parts of it. For each novel we derive a topic distribution, i.e. we calculate how strongly each topic is associated with each novel. We try different preprocessing approaches and topic numbers and build ten models for each setting to reduce the influence of randomness in LDA models. In every setting we first remove a set of predefined stop words from the novels and then use LDA on our corpus of 628 novels. The different forms of preprocessing we use are: </p> <list xmlns="http://www.tei-c.org/ns/1.0" type="unordered"> <item>no additional preprocessing</item> <item><ref target="https://github.com/MarkusKrug/NERDetection">removal of Named Entities</ref> (Jannidis et al. 2015) </item> <item><ref target="http://snowball.tartarus.org/">word stemming</ref> </item> <item><ref target="http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/">word lemmatization</ref> </item> <item>word lemmatization + removal of Named Entities </item> </list>