{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:26:53Z","timestamp":1718756813157},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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\u2019s 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\u2019s 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\u2019s plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare\u2019s works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models.<\/jats:p>","DOI":"10.1093\/llc\/fqae021","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T15:38:42Z","timestamp":1717601922000},"page":"522-531","source":"Crossref","is-referenced-by-count":0,"title":["Shakespeare Machine: New AI-Based Technologies for Textual Analysis"],"prefix":"10.1093","volume":"39","author":[{"given":"Carl","family":"Ehrett","sequence":"first","affiliation":[{"name":"Watt Family Innovation Center, Clemson University ,405 S Palmetto Blvd, Clemson, SC, USA"}]},{"given":"Lucian","family":"Ghita","sequence":"additional","affiliation":[{"name":"Department of English, Clemson University , 801 Strode Tower, Clemson, SC 29634, USA"}]},{"given":"Dillon","family":"Ranwala","sequence":"additional","affiliation":[{"name":"School of Computing, Clemson University , 100 McAdams Hall, Clemson, SC, USA"}]},{"given":"Alison","family":"Menezes","sequence":"additional","affiliation":[{"name":"School of Computing, Clemson University , 100 McAdams Hall, Clemson, SC, USA"}]}],"member":"286","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"2024061809545592500_fqae021-B1","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/socsci4030758","article-title":"Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to \u201cShakespeare Authorship Question\u201d\u2019","volume":"4","author":"Aljumily","year":"2015","journal-title":"Social Sciences"},{"key":"2024061809545592500_fqae021-B2","author":"Hylton","year":"2021"},{"key":"2024061809545592500_fqae021-B3","author":"IBM","year":"2022"},{"key":"2024061809545592500_fqae021-B4","author":"Internet Shakespeare Editions","year":"2021"},{"key":"2024061809545592500_fqae021-B5","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","article-title":"Sentiment Analysis Algorithms and Applications: A Survey","volume":"5","author":"Medhat","year":"2014","journal-title":"Ain Shams engineering journal"},{"key":"2024061809545592500_fqae021-B6","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1034\/j.1600-0706.2003.12010.x","article-title":"Arguments for Rejecting the Sequential Bonferroni in Ecological Studies","volume":"100","author":"Moran","year":"2003","journal-title":"Oikos"},{"key":"2024061809545592500_fqae021-B7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MSPEC.2020.9078455","article-title":"\u201cDeep-Speare\u201d Crafted Shakespearean Verse That Few Readers Could Distinguish from the Real Thing","volume":"57","author":"Lau","year":"2020","journal-title":"IEEE Spectrum"},{"key":"2024061809545592500_fqae021-B8","author":"Richardson","year":"2007"},{"key":"2024061809545592500_fqae021-B9","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10660-017-9257-8","article-title":"A Model for Sentiment and Emotion Analysis of Unstructured Social Media Text","volume":"18","author":"Rout","year":"2018","journal-title":"Electronic Commerce Research"},{"key":"2024061809545592500_fqae021-B10","author":"Ryskina","year":"2017"},{"key":"2024061809545592500_fqae021-B11","author":"Sanh","year":"2019"},{"key":"2024061809545592500_fqae021-B12","first-page":"3687","author":"Saravia","year":"2018"},{"key":"2024061809545592500_fqae021-B13","first-page":"2899","author":"Xu","year":"2012"},{"issue":"4","key":"2024061809545592500_fqae021-B14","first-page":"e1253","article-title":"Deep Learning for Sentiment Analysis: A Survey","volume":"8","author":"Zhang","year":"2018","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"}],"container-title":["Digital Scholarship in the Humanities"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/dsh\/article-pdf\/39\/2\/522\/58267539\/fqae021.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/dsh\/article-pdf\/39\/2\/522\/58267539\/fqae021.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T10:57:10Z","timestamp":1718708230000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/dsh\/article\/39\/2\/522\/7687917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,1]]},"references-count":14,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6,4]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/llc\/fqae021","relation":{},"ISSN":["2055-7671","2055-768X"],"issn-type":[{"value":"2055-7671","type":"print"},{"value":"2055-768X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,6]]},"published":{"date-parts":[[2024,6,1]]}}}