@inproceedings{missaoui-etal-2026-dualaxis,
title = "{D}ual{A}xis {AI} at {S}em{E}val-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis",
author = "Missaoui, Yahya and
Kebede, Solomon and
Marreddy, Mounika and
Mehler, Alexander",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.82/",
pages = "573--578",
ISBN = "979-8-89176-414-9",
abstract = "Dimensional Aspect-Based Sentiment Analy-sis models sentiment using continuous valenceand arousal scores instead of discrete polaritylabels, enabling fine-grained affect representa-tion at the aspect level. SemEval 2026 Task3 defines this setting through three subtaskscovering aspect-level regression and structuredextraction of aspect{--}opinion pairs with continu-ous scoring. We implement transformer-basedbaselines for all subtasks within a unified, re-producible framework. For aspect-level regres-sion, we fine-tune pretrained encoders in anaspect-conditioned setup to predict valence andarousal. RoBERTa-large achieves the best de-velopment performance, with average RMSEsof 0.884 (restaurant) and 0.789 (laptop)."
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<abstract>Dimensional Aspect-Based Sentiment Analy-sis models sentiment using continuous valenceand arousal scores instead of discrete polaritylabels, enabling fine-grained affect representa-tion at the aspect level. SemEval 2026 Task3 defines this setting through three subtaskscovering aspect-level regression and structuredextraction of aspect–opinion pairs with continu-ous scoring. We implement transformer-basedbaselines for all subtasks within a unified, re-producible framework. For aspect-level regres-sion, we fine-tune pretrained encoders in anaspect-conditioned setup to predict valence andarousal. RoBERTa-large achieves the best de-velopment performance, with average RMSEsof 0.884 (restaurant) and 0.789 (laptop).</abstract>
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%0 Conference Proceedings
%T DualAxis AI at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis
%A Missaoui, Yahya
%A Kebede, Solomon
%A Marreddy, Mounika
%A Mehler, Alexander
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F missaoui-etal-2026-dualaxis
%X Dimensional Aspect-Based Sentiment Analy-sis models sentiment using continuous valenceand arousal scores instead of discrete polaritylabels, enabling fine-grained affect representa-tion at the aspect level. SemEval 2026 Task3 defines this setting through three subtaskscovering aspect-level regression and structuredextraction of aspect–opinion pairs with continu-ous scoring. We implement transformer-basedbaselines for all subtasks within a unified, re-producible framework. For aspect-level regres-sion, we fine-tune pretrained encoders in anaspect-conditioned setup to predict valence andarousal. RoBERTa-large achieves the best de-velopment performance, with average RMSEsof 0.884 (restaurant) and 0.789 (laptop).
%U https://aclanthology.org/2026.semeval-1.82/
%P 573-578
Markdown (Informal)
[DualAxis AI at SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis](https://aclanthology.org/2026.semeval-1.82/) (Missaoui et al., SemEval 2026)
ACL