Brottrager, Judith ; Stahl, Annina ; Arslan, Arda ; Brandes, Ulrik ; Weitin, Thomas (2022)
Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception.
In: Journal of Computational Literary Studies, 1 (1)
doi: 10.48694/jcls.95
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
This contribution exemplifies a workflow for the quantitative operationalization and analysis of historical literary reception. We will show how to encode literary historical information in a dataset that is suitable for quantitative analysis and present a nuanced and theory-based perspective on automated sentiment detection in historical literary reviews. Applying our method to corpora of English and German novels and narratives published from 1688 to 1914 and corresponding reviews and circulating library catalogs, we investigate if a text’s popularity with lay audiences, the attention from contemporary experts or the sentiment in experts’ reviews can be predicted from textual features, with the aim of contributing to the understanding of how literary reception as a social process can be linked to textual qualities.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Brottrager, Judith ; Stahl, Annina ; Arslan, Arda ; Brandes, Ulrik ; Weitin, Thomas |
| Art des Eintrags: | Bibliographie |
| Titel: | Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | Universitäts- und Landesbibliothek Darmstadt |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Computational Literary Studies |
| Jahrgang/Volume einer Zeitschrift: | 1 |
| (Heft-)Nummer: | 1 |
| Kollation: | 27 Seiten |
| DOI: | 10.48694/jcls.95 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | This contribution exemplifies a workflow for the quantitative operationalization and analysis of historical literary reception. We will show how to encode literary historical information in a dataset that is suitable for quantitative analysis and present a nuanced and theory-based perspective on automated sentiment detection in historical literary reviews. Applying our method to corpora of English and German novels and narratives published from 1688 to 1914 and corresponding reviews and circulating library catalogs, we investigate if a text’s popularity with lay audiences, the attention from contemporary experts or the sentiment in experts’ reviews can be predicted from textual features, with the aim of contributing to the understanding of how literary reception as a social process can be linked to textual qualities. |
| Freie Schlagworte: | historical reception, operationalization, sentiment analysis, text classification, 18th century, 19th century |
| Zusätzliche Informationen: | Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 800 Literatur > 800 Literatur, Rhetorik, Literaturwissenschaft |
| Fachbereich(e)/-gebiet(e): | 02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft > Digital Philology - Neuere deutsche Literaturwissenschaft 02 Fachbereich Gesellschafts- und Geschichtswissenschaften 02 Fachbereich Gesellschafts- und Geschichtswissenschaften > Institut für Sprach- und Literaturwissenschaft |
| Hinterlegungsdatum: | 02 Aug 2024 12:49 |
| Letzte Änderung: | 02 Aug 2024 12:49 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception. (deposited 21 Feb 2023 10:12)
- Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception. (deposited 02 Aug 2024 12:49) [Gegenwärtig angezeigt]
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