@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T From Sentiment to Valence in Metaphor: a Comparison of BERT-based Sentiment and Prompted Large Language Models
%A Guolo, Rebecca
%A Martinelli, Ginevra
%A Barattieri di San Pietro, Chiara
%A Bambini, Valentina
%Y Bagdon, Christopher
%Y Vishnubhotla, Krishnapriya
%Y Lindquist, Kristen A.
%Y Ungar, Lyle
%Y Klinger, Roman
%Y Mohammad, Saif M.
%S Proceedings of Computational Affective Science (CAS) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F guolo-etal-2026-sentiment
%X 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.
%R 10.63317/42tupej9pkt5
%U https://aclanthology.org/2026.cas-1.18/
%U https://doi.org/10.63317/42tupej9pkt5
%P 212-216
Markdown (Informal)
[From Sentiment to Valence in Metaphor: a Comparison of BERT-based Sentiment and Prompted Large Language Models](https://aclanthology.org/2026.cas-1.18/) (Guolo et al., CAS 2026)
ACL