Polarity Detection of Dialectal Arabic using Deep Learning Models

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%.

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