@inproceedings{moreira-etal-2023-modeling,
title = "Modeling Readers{'} Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles",
author = "Moreira, Pascale and
Bizzoni, Yuri and
Nielbo, Kristoffer and
Lassen, Ida Marie and
Thomsen, Mads",
editor = "Akoury, Nader and
Clark, Elizabeth and
Iyyer, Mohit and
Chaturvedi, Snigdha and
Brahman, Faeze and
Chandu, Khyathi",
booktitle = "Proceedings of the 5th Workshop on Narrative Understanding",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wnu-1.5",
doi = "10.18653/v1/2023.wnu-1.5",
pages = "25--35",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="moreira-etal-2023-modeling">
<titleInfo>
<title>Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles</title>
</titleInfo>
<name type="personal">
<namePart type="given">Pascale</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuri</namePart>
<namePart type="family">Bizzoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kristoffer</namePart>
<namePart type="family">Nielbo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ida</namePart>
<namePart type="given">Marie</namePart>
<namePart type="family">Lassen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mads</namePart>
<namePart type="family">Thomsen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 5th Workshop on Narrative Understanding</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nader</namePart>
<namePart type="family">Akoury</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Clark</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohit</namePart>
<namePart type="family">Iyyer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Snigdha</namePart>
<namePart type="family">Chaturvedi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Faeze</namePart>
<namePart type="family">Brahman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khyathi</namePart>
<namePart type="family">Chandu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">moreira-etal-2023-modeling</identifier>
<identifier type="doi">10.18653/v1/2023.wnu-1.5</identifier>
<location>
<url>https://aclanthology.org/2023.wnu-1.5</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>25</start>
<end>35</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles
%A Moreira, Pascale
%A Bizzoni, Yuri
%A Nielbo, Kristoffer
%A Lassen, Ida Marie
%A Thomsen, Mads
%Y Akoury, Nader
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brahman, Faeze
%Y Chandu, Khyathi
%S Proceedings of the 5th Workshop on Narrative Understanding
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F moreira-etal-2023-modeling
%X 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.
%R 10.18653/v1/2023.wnu-1.5
%U https://aclanthology.org/2023.wnu-1.5
%U https://doi.org/10.18653/v1/2023.wnu-1.5
%P 25-35
Markdown (Informal)
[Modeling Readers’ Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles](https://aclanthology.org/2023.wnu-1.5) (Moreira et al., WNU 2023)
ACL