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    <abstract>Arabic sentiment analysis is challenged by morphological complexity and lexical variation across Arabic dialects, compounded by subjectivity in how speakers and writers express sentiment. In this paper, we present our submission for the AraSentEval 2026 Shared Task on Arabic Dialect Sentiment Analysis. We propose SARF (صرف) a multi-view architectural framework that integrates surface-level context with stemmed and rooted morphological perspectives using a shared MARBERTv2 encoder. Our system employs a hybrid BERT-CNN-BiLSTM-Attention architecture to capture both local sentiment n-grams and global sequential dependencies. Experimental results show that while individual morphological normalization strategies (stemming or rooting) may degrade performance, their joint integration via cross-morphological attention provides robust features across diverse dialects. Our final system achieved a competitive macro-F1-score of 0.9263, ranking 2nd out of 15 participating teams.</abstract>
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%0 Conference Proceedings
%T TTLab at AraSentEval: SARF( صرف) Sentiment Analysis via Root-based Fusion for Multi-Dialectal Arabic
%A Abusaleh, Ali
%A Verma, Bhuvanesh
%A Mehler, Alexander
%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 abusaleh-etal-2026-ttlab
%X Arabic sentiment analysis is challenged by morphological complexity and lexical variation across Arabic dialects, compounded by subjectivity in how speakers and writers express sentiment. In this paper, we present our submission for the AraSentEval 2026 Shared Task on Arabic Dialect Sentiment Analysis. We propose SARF (صرف) a multi-view architectural framework that integrates surface-level context with stemmed and rooted morphological perspectives using a shared MARBERTv2 encoder. Our system employs a hybrid BERT-CNN-BiLSTM-Attention architecture to capture both local sentiment n-grams and global sequential dependencies. Experimental results show that while individual morphological normalization strategies (stemming or rooting) may degrade performance, their joint integration via cross-morphological attention provides robust features across diverse dialects. Our final system achieved a competitive macro-F1-score of 0.9263, ranking 2nd out of 15 participating teams.
%R 10.63317/4wj6s3ys5osk
%U https://aclanthology.org/2026.osact-1.35/
%U https://doi.org/10.63317/4wj6s3ys5osk
%P 262-268

Markdown (Informal)

[TTLab at AraSentEval: SARF( صرف) Sentiment Analysis via Root-based Fusion for Multi-Dialectal Arabic](https://aclanthology.org/2026.osact-1.35/) (Abusaleh et al., OSACT 2026)

• TTLab at AraSentEval: SARF( صرف) Sentiment Analysis via Root-based Fusion for Multi-Dialectal Arabic (Abusaleh et al., OSACT 2026)

ACL

• Ali Abusaleh, Bhuvanesh Verma, and Alexander Mehler. 2026. TTLab at AraSentEval: SARF( صرف) Sentiment Analysis via Root-based Fusion for Multi-Dialectal Arabic. In The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks, pages 262–268, Palma, Mallorca (Spain). Association for Computational Linguistics.