Koolen, Marijn ; Zundert, Joris J. van ; Viviani, Eva ; Schnober, Carsten ; Hage, Willem van ; Tereshko, Katja (2024)
From Review to Genre to Novel and Back. An Attempt To Relate Reader Impact to Phenomena of Novel Text.
doi: 10.26083/tuprints-00027398
Report, Erstveröffentlichung, Preprint
Kurzbeschreibung (Abstract)
We are interested in the textual features that correlate with reported impact by readers of novels. We operationalize impact measurement through a rule-based reading impact model and apply it to 634,614 reader reviews mined from seven review platforms. We compute co-occurrence of impact-related terms and their keyness for genres represented in the corpus. The corpus consists of the full text of 18,885 books from which we derived topic models. The topics we find correlate strongly with genre, and we get strong indicators for what key impact terms are connected to which genre. These key impact terms gives us a first evidence-based insight into genre-related readers’ motivations.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Koolen, Marijn ; Zundert, Joris J. van ; Viviani, Eva ; Schnober, Carsten ; Hage, Willem van ; Tereshko, Katja |
| Art des Eintrags: | Erstveröffentlichung |
| Titel: | From Review to Genre to Novel and Back. An Attempt To Relate Reader Impact to Phenomena of Novel Text |
| Sprache: | Englisch |
| Publikationsjahr: | 28 Mai 2024 |
| Ort: | Darmstadt |
| (Heft-)Nummer: | 1 |
| Reihe: | CCLS2024 Conference Preprints |
| Band einer Reihe: | 3 |
| Kollation: | 26 Seiten |
| DOI: | 10.26083/tuprints-00027398 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27398 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We are interested in the textual features that correlate with reported impact by readers of novels. We operationalize impact measurement through a rule-based reading impact model and apply it to 634,614 reader reviews mined from seven review platforms. We compute co-occurrence of impact-related terms and their keyness for genres represented in the corpus. The corpus consists of the full text of 18,885 books from which we derived topic models. The topics we find correlate strongly with genre, and we get strong indicators for what key impact terms are connected to which genre. These key impact terms gives us a first evidence-based insight into genre-related readers’ motivations. |
| Freie Schlagworte: | reader impact, literary novels, genre, topic modeling |
| Status: | Preprint |
| URN: | urn:nbn:de:tuda-tuprints-273983 |
| Zusätzliche Informationen: | This paper has been submitted to the conference track of JCLS. It has been peer reviewed and accepted for presentation and discussion at the 3rd Annual Conference of Computational Literary Studies at Vienna, Austria, in June 2024. |
| 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: | 28 Mai 2024 07:57 |
| Letzte Änderung: | 03 Jun 2024 10:47 |
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