@inproceedings{iaroshenko-loukachevitch-2026-emotional,
title = "Emotional Lexicons: How Large Language Models Predict Emotional Ratings of {R}ussian Words",
author = "Iaroshenko, Polina V. and
Loukachevitch, Natalia V",
editor = "Barnes, Jeremy and
Barriere, Valentin and
De Clercq, Orph{\'e}e and
Klinger, Roman and
Nouri, C{\'e}lia and
Nozza, Debora and
Singh, Pranaydeep",
booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wassa-1.9/",
pages = "96--106",
ISBN = "979-8-89176-378-4",
abstract = "This study examines the capability of LLMs to predict emotional ratings of Russian words by comparing their assessments with both native speakers' ratings and expert evaluations. The research utilises two datasets: the ENRuN database containing associative emotional ratings of Russian nouns by native speakers, and RusEmoLex, an expert-compiled lexicon. Various open-source LLMs were evaluated, including international models (Llama-3, Qwen 2.5), Russian-developed models, and Russian-adapted variants, representing three parameter scales. The findings reveal distinct patterns in model performance: Russian-adapted models demonstrated superior alignment with native speakers' ratings, whilst model size was not a decisive factor. Conversely, larger models showed better performance in matching expert assessments, with language adaptation having minimal impact. Emotional or sensitive lexis with strong connotations produce a more substantial human-model gap."
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<abstract>This study examines the capability of LLMs to predict emotional ratings of Russian words by comparing their assessments with both native speakers’ ratings and expert evaluations. The research utilises two datasets: the ENRuN database containing associative emotional ratings of Russian nouns by native speakers, and RusEmoLex, an expert-compiled lexicon. Various open-source LLMs were evaluated, including international models (Llama-3, Qwen 2.5), Russian-developed models, and Russian-adapted variants, representing three parameter scales. The findings reveal distinct patterns in model performance: Russian-adapted models demonstrated superior alignment with native speakers’ ratings, whilst model size was not a decisive factor. Conversely, larger models showed better performance in matching expert assessments, with language adaptation having minimal impact. Emotional or sensitive lexis with strong connotations produce a more substantial human-model gap.</abstract>
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%0 Conference Proceedings
%T Emotional Lexicons: How Large Language Models Predict Emotional Ratings of Russian Words
%A Iaroshenko, Polina V.
%A Loukachevitch, Natalia V.
%Y Barnes, Jeremy
%Y Barriere, Valentin
%Y De Clercq, Orphée
%Y Klinger, Roman
%Y Nouri, Célia
%Y Nozza, Debora
%Y Singh, Pranaydeep
%S The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-378-4
%F iaroshenko-loukachevitch-2026-emotional
%X This study examines the capability of LLMs to predict emotional ratings of Russian words by comparing their assessments with both native speakers’ ratings and expert evaluations. The research utilises two datasets: the ENRuN database containing associative emotional ratings of Russian nouns by native speakers, and RusEmoLex, an expert-compiled lexicon. Various open-source LLMs were evaluated, including international models (Llama-3, Qwen 2.5), Russian-developed models, and Russian-adapted variants, representing three parameter scales. The findings reveal distinct patterns in model performance: Russian-adapted models demonstrated superior alignment with native speakers’ ratings, whilst model size was not a decisive factor. Conversely, larger models showed better performance in matching expert assessments, with language adaptation having minimal impact. Emotional or sensitive lexis with strong connotations produce a more substantial human-model gap.
%U https://aclanthology.org/2026.wassa-1.9/
%P 96-106
Markdown (Informal)
[Emotional Lexicons: How Large Language Models Predict Emotional Ratings of Russian Words](https://aclanthology.org/2026.wassa-1.9/) (Iaroshenko & Loukachevitch, WASSA 2026)
ACL