What question did this study set out to answer?

The central aim is to improve sentiment analysis in dialectal Arabic using a hybrid deep learning model.

February 22, 2026

Enhancing Dialectal Arabic Sentiment Analysis Using Deep Learning

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.

Abstract

This study aims to enhance Arabic Sentiment Analysis (ASA) by developing and evaluating a hybrid Deep Learning (DL) model, AraBERTCNNMHABiLSTM, which integrates Arabic Bidirectional Encoder Representations from Transformers (AraBERT) with Convolutional Neural Networks (CNN), Multi-Head Attention (MHA), and Bidirectional Long Short-Term Memory (BiLSTM) for Sentiment Analysis (SA) in the Iraqi Arabic dialect. Through an ablation study, we systematically assess the contribution of each component by testing the model on two datasets: "IQAD31K, " comprising 31, 324 reviews scraped from Google Play Store, and the Iraqi Arabic Dialect (IAD) dataset, comprising 2, 000 annotated comments from Iraqi Facebook pages. The complete model achieves an F1-score of 95. 63% on "IQAD31K" and 94. 50% on the IAD dataset, exceeding traditional Machine Learning (ML) methods such as Support Vector Machine (SVM) and Random Forest (RF), which struggle with Arabic linguistic complications. These findings highlight the importance of integrating AraBERT's contextual embeddings, CNN's local feature extraction, MHA's long-range dependency capture, and BiLSTM's sequential modeling to achieve the best performance in dialectal Arabic sentiment analysis, thereby contributing a robust architectural framework for future research in dialectal Arabic Natural Language Processing (ANLP).

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