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DOI: 10.14569/IJACSA.2021.0121125
PDF

Polarity Detection of Dialectal Arabic using Deep Learning Models

Author 1: Saleh M. Mohamed
Author 2: Ensaf Hussein Mohamed
Author 3: Mohamed A. Belal

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 11, 2021.

  • Abstract and Keywords
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Abstract: With the evolution of a new era of technology and social media networks, as well as an increase in Arabs sharing their point of view, it became necessary that this research be conducted. Sentiment analysis is concerned with identifying and extracting opinionated phrases from reviews or tweets. Specifically, to determine whether a given tweet is positive, negative, or neutral. Dialectical Arabic poses difficulties for sentiment analysis. In this paper, four deep learning models are presented, to be specific convolution neural networks (CNN), long short-term memory (LSTM), a hybrid of (CNN-LSTM), and Bidirectional LSTMs (BiLSTM), to determine the tweets polarities written in dialectal Arabic. The performance of the four models is validated on the used corpus with the use of word embedding and applying the (k-Fold Cross-Validation) method. The results show that CNN outperforms the others achieving an accuracy of 99.65%.

Keywords: Sentiment analysis; word embedding; sentiment classification; dialectical arabic; deep learning

Saleh M. Mohamed, Ensaf Hussein Mohamed and Mohamed A. Belal, “Polarity Detection of Dialectal Arabic using Deep Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121125

@article{Mohamed2021,
title = {Polarity Detection of Dialectal Arabic using Deep Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121125},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121125},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Saleh M. Mohamed and Ensaf Hussein Mohamed and Mohamed A. Belal}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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