Analyses of characters in dramatic works by using document embeddings

Shakespearean tragedies show clear antagonisms and the resolutions are rational which means they obey the Aristotelian unity-of-action principle. Any tragic play must rely upon its own movement. Therefore, it is complete and there should not be extra characters written only for the sake of resolution, so called”Deus-ex-Machina”. In this work, Deus-ex-Machina characters are automatically detected using machine learning methods. We first train unsupervised Doc2Vec network by using all plays of Shakespeare. Then, we collected all the lines uttered by each character in a separate document and extracted the document vectors. Thus, each character is represented with a vector in the semantic space of Shakespeare. We measure the semantic similarity between characters using the cosine difference, the angle between normalized vectors of each character document and we observe characters form a cluster. According to this work, it is possible to detect Deus-ex-Machina characters. Examples of strong unity-of-action principle plays could be demonstrated as well as distinct characters. Dis/similar characters between the plays could also be shown