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Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods

  • Research Article-Computer Engineering and Computer Science
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Abstract

With the outbreak of social networks, blogs, and forums, classifying subjective text influenced by personal feelings and opinions has become an interesting research area. Many techniques have been proposed to solve the problem of analyzing and classifying sentiments held in those reviews and recommendations. Recently, deep learning models showed promising outcomes in many fields, including sentiment analysis. Therefore in this study, we propose a sentiment analysis deep learning-based model to predict the polarity of opinions and sentiments. Two types of recurrent neural networks are leveraged to learn higher-level representations. Then to mitigate the data dependency problem and to increase the model robustness, three distinct classification algorithms were utilized to produce the final output. Experimental results proved that our model prevailed in all the selected datasets with an accuracy ranging between 81.11 and 94.32%. Moreover, the model reduced the relative classification error rate by up to 26% compared to state-of-the-art models.

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Notes

  1. the W terms denote the weight matrices, the b terms denote the bias vectors.

  2. The tensorflow software library is available at https://www.tensorflow.org

  3. The full code for DeepASA and the used datasets and other resources are available at https://zenodo.org/record/3864879#.XtDBjMBRU2x

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Authors and Affiliations

  1. Taibah University, Medina, Saudi Arabia

    Amal Alharbi

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Manal Kalkatawi & Mounira Taileb

Authors
  1. Amal Alharbi
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  2. Manal Kalkatawi
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  3. Mounira Taileb
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Correspondence to Amal Alharbi.

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Alharbi, A., Kalkatawi, M. & Taileb, M. Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods. Arab J Sci Eng 46, 8913–8923 (2021). https://doi.org/10.1007/s13369-021-05475-0

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  • DOI: https://doi.org/10.1007/s13369-021-05475-0

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