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

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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.
Titel5th Workshop on Narrative Understanding : Proceedings of the Workshop
RedaktørerNader Akoury, Elizabeth Clark, Mohit Iyyer, Snigdha Chaturvedi, Faeze Brahman, Khyathi Raghavi Chandu
Antal sider11
ForlagAssociation for Computational Linguistics
ISBN (Trykt)978-1-959429-92-0
ISBN (Elektronisk)9781959429920
StatusUdgivet - 2023
Begivenhed5th Workshop on Narrative Understanding, WNU 2023 - Toronto, Canada
Varighed: 14 jul. 2023 → …


Konference5th Workshop on Narrative Understanding, WNU 2023
Periode14/07/2023 → …


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