TU Darmstadt / ULB / TUprints

‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews

Koolen, Marijn ; Neugarten, Julia ; Boot, Peter (2023)
‘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, 2022, 1 (1)
doi: 10.26083/tuprints-00023248
Article, Secondary publication, Publisher's Version

[img] Text
jcls-104-koolen.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (440kB)
[img] Text
jcls-104-koolen.xml
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (119kB)
Item Type: Article
Type of entry: Secondary publication
Title: ‘This book makes me happy and sad and I love it’. A Rule-based Model for Extracting Reading Impact from English Book Reviews
Language: English
Date: 21 February 2023
Place of Publication: Darmstadt
Year of primary publication: 2022
Journal or Publication Title: Journal of Computational Literary Studies
Volume of the journal: 1
Issue Number: 1
Collation: 22 Seiten
DOI: 10.26083/tuprints-00023248
Corresponding Links:
Origin: Secondary publication from TUjournals
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.

Uncontrolled Keywords: reading impact, Goodreads, online book reviews, impact model
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-232483
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
Date Deposited: 21 Feb 2023 10:08
Last Modified: 22 Jul 2024 08:15
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23248
PPN:
Export:
Actions (login required)
View Item View Item