DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task

2026-10-05

AI Summary

This study addresses the challenge of sentiment analysis in highly variable Arabic dialects by introducing a novel shared task framework that integrates discriminative sentiment classification with generative sentiment polarity inversion. Methodologically, the framework comprises two subtasks—multi-dialect sentiment recognition and sentiment swapping—and constructs a unified evaluation benchmark by synthesizing multi-dialect modeling, multi-label classification, and text generation techniques. Furthermore, this work releases a high-quality multi-dialect dataset and systematically evaluates the performance of participating models in dialectal sentiment understanding and transformation. Ultimately, the proposed framework establishes a critical benchmark and introduces a new paradigm for advancing research in cross-dialectal sentiment analysis.

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.

Problem

• Arabic Dialect
• Sentiment Analysis
• Sentiment Swapping
• Natural Language Processing

Innovation

• Arabic Dialect Sentiment Analysis
• Sentiment Swapping
• Generative Task
• Shared Task
• Natural Language Processing