  There is a growing tradition in the joint field of network studies and drama
history that produces interpretations from the character networks of the
plays.The potential of such an interpretation is that the diagrams provide a
different representation of the relationships between characters as compared to
reading the text or watching the performance. Our aim is to create a method
that is able to cluster texts with similar structures on the basis of the
play's well-interpretable and simple properties, independent from the number of
characters in the drama, or in other words, the size of the network. Finding
these features is the most important part of our research, as well as
establishing the appropriate statistical procedure to calculate the
similarities between the texts. Our data was downloaded from the DraCor
database and analyzed in R (we use the GerDracor and the ShakeDraCor
sub-collection). We want to propose a robust method based on the distribution
of words among characters; distribution of characters in scenes, average length
of speech acts, or character-specific and macro-level network properties such
as clusterization coefficient and network density. Based on these metrics a
supervised classification procedure is applied to the sub-collections to
classify comedies and tragedies using the Support Vector Machine (SVM) method.
Our research shows that this approach can also produce reliable results on a
small sample size.
