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

TL;DR

This paper introduces a method to classify plays as comedies or tragedies based on structural properties of character networks, using statistical features and SVM classification, effective even with small samples.

Contribution

The study develops a robust, interpretable approach for clustering plays by genre through network analysis and statistical features, independent of character count.

Findings

Effective classification of plays into genres using network features

Method works reliably on small datasets

Identifies key structural differences between comedies and tragedies

Abstract

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.