AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic

Saad Ezzini, Shadi Abudalfa, Maram I. Alharbi, Salmane Chafik, Hamzah Luqman, Mo El-Haj, Paul Rayson, Reem Alotaibi


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 (AraSentEval), organized as part of the OSACT7 Workshop at LREC 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.
Anthology ID:
2026.osact-1.34
Volume:
The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
Month:
May
Year:
2026
Address:
Palma, Mallorca (Spain)
Editors:
Hend Al-Khalifa, Mo El-Haj, Saad Ezzini
Venues:
OSACT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–261
Language:
External URL:
https://lrec.elra.info/lrec2026-ws-osact-34
DOI:
10.63317/5ntazbi8wzad
Bibkey:
Cite (ACL):
Saad Ezzini, Shadi Abudalfa, Maram I. Alharbi, Salmane Chafik, Hamzah Luqman, Mo El-Haj, Paul Rayson, and Reem Alotaibi. 2026. AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic. In The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks, pages 256–261, Palma, Mallorca (Spain). Association for Computational Linguistics.
Cite (Informal):
AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic (Ezzini et al., OSACT 2026)
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