https://academic.oup.com/dsh/article-abstract/39/2/522/7687917
This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare’s works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts.
- Authors: Carl Ehrett, Lucian Ghita, Dillon Ranwala, Alison Menezes
- Year: 2024
- Journal: Digital Scholarship in the Humanities, Volume 39, Issue 2
- DOI: https://doi.org/10.1093/llc/fqae021