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There is a growing tradition in the joint field of network studies and dramahistory that produces interpretations from the character networks of theplays.The potential of such an interpretation is that the diagrams provide adifferent representation of the relationships between characters as compared toreading the text or watching the performance. Our aim is to create a methodthat is able to cluster texts with similar structures on the basis of theplay's well-interpretable and simple properties, independent from the number ofcharacters in the drama, or in other words, the size of the network. Findingthese features is the most important part of our research, as well asestablishing the appropriate statistical procedure to calculate thesimilarities between the texts. Our data was downloaded from the DraCordatabase and analyzed in R (we use the GerDracor and the ShakeDraCorsub-collection). We want to propose a robust method based on the distributionof words among characters; distribution of characters in scenes, average lengthof speech acts, or character-specific and macro-level network properties suchas clusterization coefficient and network density. Based on these metrics asupervised classification procedure is applied to the sub-collections toclassify comedies and tragedies using the Support Vector Machine (SVM) method.Our research shows that this approach can also produce reliable results on asmall sample size.

Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Properties