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        <title>CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science</title>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
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    <abstract>We introduce “Citizen Readers for Narrative Emotions” (CR4-NarrEmote), a large-scale, open-vocabulary dataset of narrative emotions derived through a citizen science initiative. Over a four-month period, 3,738 volunteers contributed more than 200,000 emotion annotations across 43,000 passages from long-form fiction and non-fiction, spanning 150 years, twelve genres, and multiple Anglophone cultural contexts. To facilitate model training and comparability, we provide mappings to both dimensional (Valence-Arousal-Dominance) and categorical (NRC Emotion) frameworks. We evaluate annotation reliability using lexical, categorical, and semantic agreement measures, and find substantial alignment between citizen science annotations and expert-generated labels. As the first open-vocabulary resource focused on narrative emotions at scale, CR4-NarrEmote provides an important foundation for affective computing and narrative understanding.</abstract>
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    <identifier type="doi">10.18653/v1/2025.emnlp-main.493</identifier>
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%0 Conference Proceedings
%T CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science
%A Piper, Andrew
%A Budac, Robert
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F piper-budac-2025-cr4
%X We introduce “Citizen Readers for Narrative Emotions” (CR4-NarrEmote), a large-scale, open-vocabulary dataset of narrative emotions derived through a citizen science initiative. Over a four-month period, 3,738 volunteers contributed more than 200,000 emotion annotations across 43,000 passages from long-form fiction and non-fiction, spanning 150 years, twelve genres, and multiple Anglophone cultural contexts. To facilitate model training and comparability, we provide mappings to both dimensional (Valence-Arousal-Dominance) and categorical (NRC Emotion) frameworks. We evaluate annotation reliability using lexical, categorical, and semantic agreement measures, and find substantial alignment between citizen science annotations and expert-generated labels. As the first open-vocabulary resource focused on narrative emotions at scale, CR4-NarrEmote provides an important foundation for affective computing and narrative understanding.
%R 10.18653/v1/2025.emnlp-main.493
%U https://aclanthology.org/2025.emnlp-main.493/
%U https://doi.org/10.18653/v1/2025.emnlp-main.493
%P 9784-9795

Markdown (Informal)

[CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science](https://aclanthology.org/2025.emnlp-main.493/) (Piper & Budac, EMNLP 2025)

• CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science (Piper & Budac, EMNLP 2025)

ACL

• Andrew Piper and Robert Budac. 2025. CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9784–9795, Suzhou, China. Association for Computational Linguistics.