Genre Analysis and Corpus Design - Henny-Krahmer, Ulrike
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This work in the field of digital literary stylistics and computational literary studies is concerned with theoretical concerns of literary genre, with the design of a corpus of nineteenth-century Spanish-American novels, and with its empirical analysis in terms of subgenres of the novel. The digital text corpus consists of 256 Argentine, Cuban, and Mexican novels from the period between 1830 and 1910. It has been created with the goal to analyze thematic subgenres and literary currents that were represented in numerous novels in the nineteenth century by means of computational text…mehr

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Produktbeschreibung
This work in the field of digital literary stylistics and computational literary studies is concerned with theoretical concerns of literary genre, with the design of a corpus of nineteenth-century Spanish-American novels, and with its empirical analysis in terms of subgenres of the novel. The digital text corpus consists of 256 Argentine, Cuban, and Mexican novels from the period between 1830 and 1910. It has been created with the goal to analyze thematic subgenres and literary currents that were represented in numerous novels in the nineteenth century by means of computational text categorization methods. To categorize the texts, statistical classification and a family resemblance analysis relying on network analysis are used with the aim to examine how the subgenres, which are understood as communicative, conventional phenomena, can be captured on the stylistic, textual level of the novels that participate in them.
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Autorenporträt
Ulrike Henny-Krahmer has been a Junior Professor for Digital Humanities at the University of Rostock, Germany since 2021. She received her doctorate from the University of Würzburg for her work in the field of computational literary studies on the computational analysis of subgenres of the nineteenth-century Spanish-American novel. In the field of digital humanities, her research interests also extend to digital scholarly editions, applications of machine learning methods in the humanities, and the evaluation and sustainability of digital research results.