Brottrager, Judith ; Stahl, Annina ; Arslan, Arda ; Brandes, Ulrik ; Weitin, Thomas (2023)
Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception.
In: Journal of Computational Literary Studies, 2022, 1 (1)
doi: 10.26083/tuprints-00023250
Article, Secondary publication, Publisher's Version
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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: | 2023 |
| Creators: | Brottrager, Judith ; Stahl, Annina ; Arslan, Arda ; Brandes, Ulrik ; Weitin, Thomas |
| Type of entry: | Secondary publication |
| Title: | Modeling and Predicting Literary Reception. A Data-Rich Approach to Literary Historical Reception |
| Language: | English |
| Date: | 2023 |
| Place of Publication: | Darmstadt |
| Year of primary publication: | 2022 |
| 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: | 10.26083/tuprints-00023250 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23250 |
| Corresponding Links: | |
| Origin: | Secondary publication from TUjournals |
| 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 |
| Status: | Publisher's Version |
| URN: | urn:nbn:de:tuda-tuprints-232508 |
| 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: | 21 Feb 2023 10:12 |
| Last Modified: | 27 Feb 2023 12:44 |
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| Suche nach Titel in: | TUfind oder in Google |
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