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, Primary publication, Preprint
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.
| Item Type: | Report |
|---|---|
| Erschienen: | 2024 |
| Creators: | Koolen, Marijn ; Zundert, Joris J. van ; Viviani, Eva ; Schnober, Carsten ; Hage, Willem van ; Tereshko, Katja |
| Type of entry: | Primary publication |
| Title: | From Review to Genre to Novel and Back. An Attempt To Relate Reader Impact to Phenomena of Novel Text |
| Language: | English |
| Date: | 28 May 2024 |
| Place of Publication: | Darmstadt |
| Issue Number: | 1 |
| Series: | CCLS2024 Conference Preprints |
| Series Volume: | 3 |
| Collation: | 26 Seiten |
| DOI: | 10.26083/tuprints-00027398 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27398 |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | reader impact, literary novels, genre, topic modeling |
| Status: | Preprint |
| URN: | urn:nbn:de:tuda-tuprints-273983 |
| Additional Information: | 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. |
| 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: | 28 May 2024 07:57 |
| Last Modified: | 03 Jun 2024 10:47 |
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