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Sentiment Analysis (SA) discerns the subjective tone within text, categorizing it as positive, neutral, or negative. Arabic Sentiment Analysis (ASA) has distinct obstacles owing to the language’s intricate morphology, many dialects, and elaborate linguistic frameworks. This study compares SA models for Arabic text across multiple datasets, evaluating traditional machine learning (ML) algorithms, such as Random Forest (RF) and Support Vector Machine (SVM); deep learning (DL) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU); and transformer-based models like BERT, AraBERT, and XLM-RoBERTa. Experiments on datasets—HARD, Khooli, AJGT, and Ar-Tweet—covering MSA and dialects such as Gulf and Egyptian demonstrate that transformer-based models, particularly AraBERT v02, achieve the highest accuracy of 93.9% on the HARD dataset. The study highlights the significance of dataset characteristics and the advantages of advanced models, offering valuable insights into Arabic NLP and advancing SA research.

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A Comparative Evaluation of ML, DL, and Transformer Models in Arabic Sentiment Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Amani A. Aladeemy,
  • Sachin N. Deshmukh

摘要

Sentiment Analysis (SA) discerns the subjective tone within text, categorizing it as positive, neutral, or negative. Arabic Sentiment Analysis (ASA) has distinct obstacles owing to the language’s intricate morphology, many dialects, and elaborate linguistic frameworks. This study compares SA models for Arabic text across multiple datasets, evaluating traditional machine learning (ML) algorithms, such as Random Forest (RF) and Support Vector Machine (SVM); deep learning (DL) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU); and transformer-based models like BERT, AraBERT, and XLM-RoBERTa. Experiments on datasets—HARD, Khooli, AJGT, and Ar-Tweet—covering MSA and dialects such as Gulf and Egyptian demonstrate that transformer-based models, particularly AraBERT v02, achieve the highest accuracy of 93.9% on the HARD dataset. The study highlights the significance of dataset characteristics and the advantages of advanced models, offering valuable insights into Arabic NLP and advancing SA research.