Shakespeare Machine: New AI-Based Technologies for Textual Analysis.
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 anal...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 2; pp. 522 - 532 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=177947265&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 177947265 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2024 vid: 39 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177947265 10.1093/llc/fqae021 ppf: 522 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P size: 504KB tig: atl: Shakespeare Machine: New AI-Based Technologies for Textual Analysis. aug: au: Ehrett, Carl Ghita, Lucian Ranwala, Dillon Menezes, Alison affil: Watt Family Innovation Center, Clemson University ,405 S Palmetto Blvd, Clemson, SC, USA Department of English, Clemson University , 801 Strode Tower, Clemson, SC 29634, USA School of Computing, Clemson University , 100 McAdams Hall, Clemson, SC, USA su: Shakespeare, William, 1564-1616 Language models Natural language processing Content analysis Metadata Pattern matching Patterns (Mathematics) Handwriting recognition (Computer science) sug: subj: Shakespeare, William, 1564-1616 Language models Natural language processing Content analysis Metadata Pattern matching Patterns (Mathematics) Handwriting recognition (Computer science) keyword: artificial intelligence gender large language models natural language processing sentiment analysis Shakespeare ab: 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. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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