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Arabic sentiment analysis using recurrent neural networks: a review

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Abstract

Over the last decade, the amount of Arabic content created on websites and social media has grown significantly. Opinions are shared openly and freely on social media and thus provide a rich source for trend analyses, which are accomplished by conventional methods of language interpretation, such as sentiment analysis. Due to its accuracy in studying unstructured data, deep learning has been increasingly used to test opinions. Recurrent neural networks (RNNs) are a promising approach in textual analysis and exhibit large morphological variations. In total, 193 studies used RNNs in English-language sentiment analysis, and 24 studies used RNNs in Arabic-language sentiment analysis. Those studies varied in the areas they address, the functionality and weaknesses of the models, and the number and scale of the available datasets for different dialects. Such variations are worthy of attention and monitoring; thus, this paper presents a systematic examination of the literature to label, evaluate, and identify state-of-the-art studies using RNNs for Arabic sentiment analysis.

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Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding and supporting this work through Graduate Students Research Support Program.

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    Sarah Omar Alhumoud & Asma Ali Al Wazrah

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Alhumoud, S.O., Al Wazrah, A.A. Arabic sentiment analysis using recurrent neural networks: a review. Artif Intell Rev 55, 707–748 (2022). https://doi.org/10.1007/s10462-021-09989-9

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