DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task

Saad Ezzini; Shadi Abudalfa; Maram Alharbi; Salmane Chafik; Hind Alatawi; Mo El-Haj; Ahmed Abdelali; Osamah Alnahari

cs.CLOct 5, 2026

DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task

Authors: Saad Ezzini, Shadi Abudalfa, Maram Alharbi, Salmane Chafik, Hind Alatawi, Mo El-Haj, Ahmed Abdelali, Osamah Alnahari, +1 more

Organizations: King Fahd University of Petroleum and Minerals, KSA · Onaizah Colleges, KSA · University College of Applied Sciences, Palestine · Lancaster University, UK · UM6P, Morocco · VinUniversity, Vietnam · El Technology, Qatar · University of Luxembourg, Luxembourg

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.

Figures & tables

Figure 1: Data creation process for both subtasks.

#	Team Name	CodaBench Username	Macro F1
1	ALEXIS	momen_ibrahim-870387	0.8667
2	Axiom	ajwad_hossain-867052	0.8210
3	PTUK-NLP	tasneemduridi-857223	0.8174
4	NU-SentEval	habiba_ayman-867381	0.8140
5	Gradient Descenders	normalman159-857755	0.8010
6	LahjaSensei	ahmadalmoustafa-856400	0.7977

Table 1: Official evaluation results for Subtask 1: Arabic Dialect Sentiment Analysis.

#	Team Name	CodaBench Username	Sentiment Style Accuracy	BLEU	chrF
1	ALEXIS	momen_ibrahim-870527	0.9581	56.91	72.18
2	AKARROUCH	akarrouch-871040	0.7851	49.82	70.12
3	LahjaSensei	ahmadalmoustafa-865064	0.7789	41.48	64.45
4	NAMAA	mona_-871054	0.7597	51.58	71.02
5	–	melmoussaoui-870973	0.7530	49.03	69.53
6	Sabaa	mohamedsabaa-855812	0.7447	49.07	69.33

Table 2: Official evaluation results for Subtask 2: Arabic Sentiment Swap.

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BARRAC: Adaptation of an English Aspect-based Sentiment Analysis Approach for Classification Tasks in Arabic Dialects

Ali Almutairi, Gelareh Mohammadi, Imran Razzak, Aditya Joshi

University of New South Wales, Australia · MBZUAI, UAE

Arabic Natural Language Processing Arabic Sentiment Representation Learning

Open paper AskPDF arXiv

With the rapid growth of Arabic NLP, several models, datasets and benchmarks have been reported. This paper asks whether approaches developed for majority languages like English can be adapted to Arabic tasks. We adapt an English aspect-based sentiment analysis framework to Arabic classification tasks and present the adaptation as BARRAC: Brainstorming Alignment and Replaced Representation learning for ArabiC tasks. BARRAC replaces consumer-review attribute pools with Arabic linguistic devices and markers for dialectal sentiment, sarcasm, and dialect identification, and replaces noisy self-training with two-stage training. Evaluated on five Arabic dialect datasets, BARRAC achieves a mean macro-F1 of 63.93%, outperforming the best few-label SOTA by 3%, and outperforming GPT-4o on four out of five tasks. Error analysis provides insights into remaining challenges. These results demonstrate that adapting task-specific approaches is a promising direction for Arabic NLP alongside adapting models, datasets and benchmarks.

Abstract

Explore similar work

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