Stylometric analysis of characters in Shakespeare's plays.

Playwrights and screenwriters compose dialogues with characters from both genders. Assuming that men and women speak or write differently, can a great author take account of this difference? Previous studies have ascertained some stylistic markers that can discriminate between men and women either i...

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Publicado en:Digital Scholarship in the Humanities Vol. 38; no. 3; pp. 1238 - 1247
Autor principal: Savoy, Jacques
Formato: Artículo
Publicado: Oxford University Press / USA Sep2023
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Stylometric analysis of characters in Shakespeare's plays.
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        au: Savoy, Jacques
        affil: Computer Science Department, University of Neuchatel, rue Emile Argand 11 , Neuchatel , 2000, Switzerland
      su:
        Shakespeare, William, 1564-1616
        Random forest algorithms
        Logistic regression analysis
        Women's writings
        Screenwriters
        Gender
      sug:
        subj:
          Shakespeare, William, 1564-1616
          Random forest algorithms
          Logistic regression analysis
          Women's writings
          Screenwriters
          Gender
      ab: Playwrights and screenwriters compose dialogues with characters from both genders. Assuming that men and women speak or write differently, can a great author take account of this difference? Previous studies have ascertained some stylistic markers that can discriminate between men and women either in writing or oral productions. The main aim of this study is to verify whether in Shakespeare's plays female figures are talking in a distinct way compared with male ones. First, this study confirms the effectiveness of a set of predefined stylistic features using a tweet dataset extracted from CLEF-PAN corpora. Second, we demonstrate that the two genders present distinct styles when analyzing twenty-nine of Shakespeare's plays, demonstrating that the author adopts a distinct style for each gender. Our experiments are grounded on two efficient classifiers (logistic regression and random forest) able to automatically identify short passages (500 or 1,000 word-tokens) corresponding to either a male or a female utterance with an accuracy of around 80%.
      pubtype: Academic Journal
      doctype: Article
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    language: English
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