An Empirical Deep Learning Approach for Arabic News Classification

In this paper, we tackle the problem of Arabic news classification. A dataset of 5,000 news articles from various Saudi Arabian news sources were gathered, classified into six categories: business, entertainment, health, politics, sports, and technology. We conducted experiments using different pre-processing techniques, word embeddings, and deep learning architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, as well as a hybrid CNN-LSTM model. Our proposed model achieved an accuracy of 93.15, outperforming other models in terms of accuracy. Moreover, our model is evaluated on other Arabic news datasets and obtained competitive results. Our approach demonstrates the effectiveness of deep learning methods in Arabic news classification and emphasizes the significance of careful selection of preprocessing techniques, word embeddings, and deep learning architectures.

@article{Alroobaea2023,
  title     = {An Empirical Deep Learning Approach for Arabic News Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Roobaea Alroobaea},
  doi       = {10.14569/IJACSA.2023.01406112},
  url       = {https://doi.org/10.14569/IJACSA.2023.01406112}
}