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<p>The rise of Arabic dialects on social media has made sentiment analysis essential for understanding regional opinions. However, most existing research focuses on high-resource languages, leaving low-resource dialects like Algerian Arabic underexplored. To address this, we introduced a publicly available dataset of 18,589 Algerian dialect tweets and presented a customized preprocessing pipeline tailored to the dialect’s unique linguistic features. We further conducted a comprehensive evaluation of sentiment analysis models, where our best-performing MARBERT-LSTM model achieved 91.23% accuracy, setting a new benchmark. This work provides both a valuable resource and a strong baseline for future research in dialectal Arabic NLP.</p>

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Benchmarking sentiment analysis of algerian arabic dialect on X (Twitter)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月9日

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  • Lamia Ouchene,
  • Sadik Bessou

摘要

The rise of Arabic dialects on social media has made sentiment analysis essential for understanding regional opinions. However, most existing research focuses on high-resource languages, leaving low-resource dialects like Algerian Arabic underexplored. To address this, we introduced a publicly available dataset of 18,589 Algerian dialect tweets and presented a customized preprocessing pipeline tailored to the dialect’s unique linguistic features. We further conducted a comprehensive evaluation of sentiment analysis models, where our best-performing MARBERT-LSTM model achieved 91.23% accuracy, setting a new benchmark. This work provides both a valuable resource and a strong baseline for future research in dialectal Arabic NLP.