DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task

Saad Ezzini, Shadi Abudalfa, Maram Alharbi, Salmane Chafik, Hind Alatawi +4 more
10/5/2026
cs.CLcs.AI

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Abstract

Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic Dialects (DialectSentEval), hosted with the Arabic Natural Language Processing Conference (ArabicNLP 2026). This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.

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Cite this paper

@article{ezzini2026dialectsenteval,
  title  = {DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task},
  author = {Saad Ezzini and Shadi Abudalfa and Maram Alharbi and Salmane Chafik and Hind Alatawi and Mo El-Haj and Ahmed Abdelali and Osamah Alnahari and Salima Lamsiyah},
  year   = {2026},
  eprint = {2610.06298},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.06298v1}
}

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