@inproceedings{ezzini-etal-2026-arasenteval,
title = "{A}ra{S}ent{E}val 2026: A Shared Task on Sentiment Analysis and Swapping in {A}rabic",
author = "Ezzini, Saad and
Abudalfa, Shadi and
Alharbi, Maram I. and
Chafik, Salmane and
Luqman, Hamzah and
El-Haj, Mo and
Rayson, Paul and
Alotaibi, Reem",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.34/",
doi = "10.63317/5ntazbi8wzad",
pages = "256--261",
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."
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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 (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.</abstract>
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%0 Conference Proceedings
%T AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic
%A Ezzini, Saad
%A Abudalfa, Shadi
%A Alharbi, Maram I.
%A Chafik, Salmane
%A Luqman, Hamzah
%A El-Haj, Mo
%A Rayson, Paul
%A Alotaibi, Reem
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F ezzini-etal-2026-arasenteval
%X 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.
%R 10.63317/5ntazbi8wzad
%U https://aclanthology.org/2026.osact-1.34/
%U https://doi.org/10.63317/5ntazbi8wzad
%P 256-261
Markdown (Informal)
[AraSentEval 2026: A Shared Task on Sentiment Analysis and Swapping in Arabic](https://aclanthology.org/2026.osact-1.34/) (Ezzini et al., OSACT 2026)
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.