TU Darmstadt / ULB / TUbiblio

Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception

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
Article, Bibliographie

This is the latest version of this item.

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.

Item Type: Article
Erschienen: 2022
Creators: Brottrager, Judith ; Stahl, Annina ; Arslan, Arda ; Brandes, Ulrik ; Weitin, Thomas
Type of entry: Bibliographie
Title: Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception
Language: English
Date: 2022
Place of Publication: Darmstadt
Publisher: Universitäts- und Landesbibliothek Darmstadt
Journal or Publication Title: Journal of Computational Literary Studies
Volume of the journal: 1
Issue Number: 1
Collation: 27 Seiten
DOI: 10.48694/jcls.95
Corresponding Links:
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.

Uncontrolled Keywords: historical reception, operationalization, sentiment analysis, text classification, 18th century, 19th century
Additional Information:

Urspr. Konferenzveröffentlichung/Originally conference publication: 1st Annual Conference of Computational Literary Studies, 01.-02.06.2022, Darmstadt, Germany

Classification DDC: 800 Literature > 800 Literature, rhetoric and criticism
Divisions: 02 Department of History and Social Science > Institut für Sprach- und Literaturwissenschaft > Digital Philology – Modern German Literary Studies
02 Department of History and Social Science
02 Department of History and Social Science > Institut für Sprach- und Literaturwissenschaft
Date Deposited: 02 Aug 2024 12:49
Last Modified: 02 Aug 2024 12:49
PPN:
Export:
Suche nach Titel in: TUfind oder in Google

Available Versions of this Item

Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details