Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles

Pascale Moreira, Yuri Bizzoni, Kristoffer Nielbo, Ida Marie Lassen, Mads Thomsen

Abstract
Predicting literary quality and reader appreciation of narrative texts are highly complex challenges in quantitative and computational literary studies due to the fluid definitions of quality and the vast feature space that can be considered when modeling a literary work. This paper investigates the potential of sentiment arcs combined with topical-semantic profiling of literary narratives as indicators for their literary quality. Our experiments focus on a large corpus of 19th and 20the century English language literary fiction, using GoodReads’ ratings as an imperfect approximation of the diverse range of reader evaluations and preferences. By leveraging a stacked ensemble of regression models, we achieve a promising performance in predicting average readers’ scores, indicating the potential of our approach in modeling literary quality.
Anthology ID:
2023.wnu-1.5
Volume:
Proceedings of the 5th Workshop on Narrative Understanding
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Nader Akoury, Elizabeth Clark, Mohit Iyyer, Snigdha Chaturvedi, Faeze Brahman, Khyathi Chandu
Venue:
WNU
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–35
Language:
URL:
https://aclanthology.org/2023.wnu-1.5
DOI:
10.18653/v1/2023.wnu-1.5
Bibkey:
Cite (ACL):
Pascale Moreira, Yuri Bizzoni, Kristoffer Nielbo, Ida Marie Lassen, and Mads Thomsen. 2023. Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles. In Proceedings of the 5th Workshop on Narrative Understanding, pages 25–35, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles (Moreira et al., WNU 2023)
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