Analyses of Literary Texts by Using Statistical Inference Methods

Abstract

If a road map had to be drawn for Computational Criticism and subsequent Artificial Literature, it would have certainly considered Shakespearean plays. Demonstration of these structures through text analysis can be seen as both a naive effort and a scientific view of the characteristics of the texts. In this study, the textual analysis of Shakespeare plays was carried out for this purpose. Methodologically, we consecutively use Latent Dirichlet Allocation (LDA) and Singular Value Decomposition (SVD) in order to extract topics and then reduce topic distribution over documents into two-dimensional space. The first question asks if there is a genre called Romance between Comedy and Tragedy plays. The second question is, if each character’s speech is taken as a text, whether the dramatic relationship between them can be revealed. Consequently, we find relationships between genres, also verified by literary theory and the main characters follow the antagonisms within the play as the length of speech increases. Although the results of the classification of the side characters in the plays are not always what one would have expected based on the reading of the plays, there are observations on dramatic fiction, which is also verified by literary theory. Tragedies and revenge dramas have different character groupings.