@inproceedings{piper-budac-2025-cr4,
    title = "{CR}4-{N}arr{E}mote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science",
    author = "Piper, Andrew  and
      Budac, Robert",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.493/",
    pages = "9784--9795",
    ISBN = "979-8-89176-332-6",
    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."
}