Koolen, Marijn ; Neugarten, Julia ; Boot, Peter (2022)
‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews.
In: Journal of Computational Literary Studies, 1 (1)
doi: 10.48694/jcls.104
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Being able to identify and analyse reading impact expressed in online book reviews allows us to investigate how people read books and how books affect their readers. In this paper we investigate the feasibility of creating an English translation of a rule-based reading impact model for reviews of Dutch fiction. We extend the model with additional rules and categories to measure reading impact in terms of positive and negative feeling, narrative and stylistic impact, humour, surprise, attention, and reflection. We created ground truth annotations to evaluate the model and found that the translated rules and new impact categories are effective in identifying certain types of reading impact expressed in English book reviews. However, for some types of impact the rules are inaccurate, and for most categories they are incomplete. Additional rules are needed to improve recall, which could potentially be enhanced by incorporating Machine Learning. At the same time, we conclude that some impact aspects are hard to extract with a rule-based model. When applying the model to a large set of reviews, lists of the top-scoring books in the impact categories show the model’s prima-facie validity. Correlations among the categories include some that make sense and others that require further research. Overall, the evidence suggests that for investigating the impact of books, manually formulated rules are partially successful, and are probably best used in a hybrid approach.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Koolen, Marijn ; Neugarten, Julia ; Boot, Peter |
| Art des Eintrags: | Bibliographie |
| Titel: | ‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews |
| 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: | 22 Seiten |
| DOI: | 10.48694/jcls.104 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Being able to identify and analyse reading impact expressed in online book reviews allows us to investigate how people read books and how books affect their readers. In this paper we investigate the feasibility of creating an English translation of a rule-based reading impact model for reviews of Dutch fiction. We extend the model with additional rules and categories to measure reading impact in terms of positive and negative feeling, narrative and stylistic impact, humour, surprise, attention, and reflection. We created ground truth annotations to evaluate the model and found that the translated rules and new impact categories are effective in identifying certain types of reading impact expressed in English book reviews. However, for some types of impact the rules are inaccurate, and for most categories they are incomplete. Additional rules are needed to improve recall, which could potentially be enhanced by incorporating Machine Learning. At the same time, we conclude that some impact aspects are hard to extract with a rule-based model. When applying the model to a large set of reviews, lists of the top-scoring books in the impact categories show the model’s prima-facie validity. Correlations among the categories include some that make sense and others that require further research. Overall, the evidence suggests that for investigating the impact of books, manually formulated rules are partially successful, and are probably best used in a hybrid approach. |
| Freie Schlagworte: | reading impact, Goodreads, online book reviews, impact model |
| 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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‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews. (deposited 21 Feb 2023 10:08)
- ‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews. (deposited 02 Aug 2024 12:49) [Gegenwärtig angezeigt]
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