@inproceedings{ohman-etal-2024-emotionarcs,
title = "{E}motion{A}rcs: Emotion Arcs for 9,000 Literary Texts",
author = "Ohman, Emily and
Bizzoni, Yuri and
Feldkamp Moreira, Pascale and
Nielbo, Kristoffer",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.7",
pages = "51--66",
abstract = "We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English novels, assembled to understand the dynamics of emotions represented in text and how these emotions may influence a novel ́s reception and perceived quality. We evaluate emotion arcs manually, by comparing them to human annotation and against other similar emotion modeling systems to show that our system produces coherent emotion arcs that correspond to human interpretation. We present and make this resource available for further studies of a large collection of emotion arcs and present one application, exploring these arcs for modeling reader appreciation. Using information-theoretic measures to analyze the impact of emotions on literary quality, we find that emotional entropy, as well as the skewness and steepness of emotion arcs correlate with two proxies of literary reception. Our findings may offer insights into how quality assessments relate to emotional complexity and could help with the study of affect in literary novels.",
}
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<abstract>We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English novels, assembled to understand the dynamics of emotions represented in text and how these emotions may influence a novel ́s reception and perceived quality. We evaluate emotion arcs manually, by comparing them to human annotation and against other similar emotion modeling systems to show that our system produces coherent emotion arcs that correspond to human interpretation. We present and make this resource available for further studies of a large collection of emotion arcs and present one application, exploring these arcs for modeling reader appreciation. Using information-theoretic measures to analyze the impact of emotions on literary quality, we find that emotional entropy, as well as the skewness and steepness of emotion arcs correlate with two proxies of literary reception. Our findings may offer insights into how quality assessments relate to emotional complexity and could help with the study of affect in literary novels.</abstract>
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%0 Conference Proceedings
%T EmotionArcs: Emotion Arcs for 9,000 Literary Texts
%A Ohman, Emily
%A Bizzoni, Yuri
%A Feldkamp Moreira, Pascale
%A Nielbo, Kristoffer
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%S Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F ohman-etal-2024-emotionarcs
%X We introduce EmotionArcs, a dataset comprising emotional arcs from over 9,000 English novels, assembled to understand the dynamics of emotions represented in text and how these emotions may influence a novel ́s reception and perceived quality. We evaluate emotion arcs manually, by comparing them to human annotation and against other similar emotion modeling systems to show that our system produces coherent emotion arcs that correspond to human interpretation. We present and make this resource available for further studies of a large collection of emotion arcs and present one application, exploring these arcs for modeling reader appreciation. Using information-theoretic measures to analyze the impact of emotions on literary quality, we find that emotional entropy, as well as the skewness and steepness of emotion arcs correlate with two proxies of literary reception. Our findings may offer insights into how quality assessments relate to emotional complexity and could help with the study of affect in literary novels.
%U https://aclanthology.org/2024.latechclfl-1.7
%P 51-66
Markdown (Informal)
[EmotionArcs: Emotion Arcs for 9,000 Literary Texts](https://aclanthology.org/2024.latechclfl-1.7) (Ohman et al., LaTeCHCLfL-WS 2024)
ACL
- Emily Ohman, Yuri Bizzoni, Pascale Feldkamp Moreira, and Kristoffer Nielbo. 2024. EmotionArcs: Emotion Arcs for 9,000 Literary Texts. In Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), pages 51–66, St. Julians, Malta. Association for Computational Linguistics.