@inproceedings{guolo-etal-2026-sentiment,
    title = "From Sentiment to Valence in Metaphor: a Comparison of {BERT}-based Sentiment and Prompted Large Language Models",
    author = "Guolo, Rebecca  and
      Martinelli, Ginevra  and
      Barattieri di San Pietro, Chiara  and
      Bambini, Valentina",
    editor = "Bagdon, Christopher  and
      Vishnubhotla, Krishnapriya  and
      Lindquist, Kristen A.  and
      Ungar, Lyle  and
      Klinger, Roman  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of Computational Affective Science ({CAS}) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.cas-1.18/",
    doi = "10.63317/42tupej9pkt5",
    pages = "212--216",
    abstract = "Although the affective dimension is a key aspect of metaphor, computational studies of figurative language have largely overlooked psycholinguistic variables such as valence. This study investigates whether computational models can reliably estimate the affective aspects of Italian and German metaphors and whether metaphor valence is compositionally derived. Outputs of BERT-based sentiment analysis and a valence-prompted LLM were compared with human ratings. Results show that the former exhibit limited alignment with human judgments, whereas higher agreement is achieved when the explicit concept of valence is prompted in a LLM. Both humans and models rely on the combined valence of the individual lemmas, suggesting a compositional contribution to metaphor valence."
}