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February 22, 2026ACM Transactions on Asian and Low-Resource Language Information Processing0 citations

Enhancing Arabic Sentiment Analysis Using Deep Learning Techniques

Enhancing Dialectal Arabic Sentiment Analysis Using Deep Learning

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Authors

AAAbbas Raza AliNBNecaattin Barışçı

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Overview

Develops a hybrid deep learning model to improve sentiment analysis in Iraqi Arabic, indicating better performance than traditional methods.

Key Points

  • The central aim is to improve sentiment analysis in dialectal Arabic using a hybrid deep learning model.
  • Developed a hybrid model combining AraBERT, CNN, MHA, and BiLSTM.
  • Performed an ablation study to evaluate each component's contribution.
  • Tested on two datasets: IQAD31K and IAD, with varying review amounts.
  • Compared performance against traditional machine learning methods like SVM and RF.
  • Achieved an F1-score of 95.63% on the IQAD31K dataset.
  • Obtained an F1-score of 94.50% on the IAD dataset.
  • Exceeded the performance of traditional ML methods, highlighting effective integration of deep learning components.

Cite This Study

Ali et al. (2026) studied this question.

synapsesocial.com/papers/699a9d8e482488d673cd3796https://doi.org/10.1145/3798049
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