@inproceedings{bizzoni-etal-2023-sentimental,
title = "Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features",
author = "Bizzoni, Yuri and
Moreira, Pascale and
Thomsen, Mads Rosendahl and
Nielbo, Kristoffer",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.2",
doi = "10.18653/v1/2023.wassa-1.2",
pages = "11--18",
abstract = "Over the years, the task of predicting reader appreciation or literary quality has been the object of several studies, but it remains a challenging problem in quantitative literary studies and computational linguistics alike, as its definition can vary a lot depending on the genre, the adopted features and the annotation system. This paper attempts to evaluate the impact of sentiment arc modelling versus more classical stylometric features for user-ratings of novels. We run our experiments on a corpus of English language narrative literary fiction from the 19th and 20th century, showing that syntactic and surface-level features can be powerful for the study of literary quality, but can be outperformed by sentiment-characteristics of a text.",
}
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%0 Conference Proceedings
%T Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features
%A Bizzoni, Yuri
%A Moreira, Pascale
%A Thomsen, Mads Rosendahl
%A Nielbo, Kristoffer
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bizzoni-etal-2023-sentimental
%X Over the years, the task of predicting reader appreciation or literary quality has been the object of several studies, but it remains a challenging problem in quantitative literary studies and computational linguistics alike, as its definition can vary a lot depending on the genre, the adopted features and the annotation system. This paper attempts to evaluate the impact of sentiment arc modelling versus more classical stylometric features for user-ratings of novels. We run our experiments on a corpus of English language narrative literary fiction from the 19th and 20th century, showing that syntactic and surface-level features can be powerful for the study of literary quality, but can be outperformed by sentiment-characteristics of a text.
%R 10.18653/v1/2023.wassa-1.2
%U https://aclanthology.org/2023.wassa-1.2
%U https://doi.org/10.18653/v1/2023.wassa-1.2
%P 11-18
Markdown (Informal)
[Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features](https://aclanthology.org/2023.wassa-1.2) (Bizzoni et al., WASSA 2023)
ACL