Arabic News Articles Classification Using Different Word Embeddings

With the accelerated growth of the internet, vast repositories of unstructured textual data have emerged, necessitating automated categorization algorithms for organization and insight extraction. The Arabic language, however, poses particular challenges due to its inflected nature, large vocabulary, and varying forms. This study targets the development of robust automated classification systems for Arabic text, a language increasingly adopted online. In this paper, we propose a comparison of four prevalent pre-trained word embeddings: Word2Vec (represented by Aravec), GloVe, FastText, and BERT (represented by ARBERTv2), using the widely-adopted SANAD dataset of Arabic news articles. We provide a comprehensive comparison by applying a fixed deep learning architecture across all four word embeddings to ensure fairness. The motivation behind this comparison is to bridge the knowledge gap observed in the usage of popular word embeddings for Arabic news classification. Despite the state-of-the-art results from transformer models, a significant inclination towards older methodologies still persists. Hence, we aim to highlight the efficiencies of modern techniques. Results indicate that ARBERTv2 outperforms the other embeddings, achieving 95.81%, 98.68%, and 99.30% accuracy on the Akhbarona, Alkhaleej, and Alarabiya subsets of SANAD, respectively. Despite its large number of parameters, ARBERT’s context-based word embeddings seem to offer superior performance. FastText stood out as the top performer among non-contextualized word embeddings due to its ability to capture morphological similarities and handle out-of-vocabulary words. Following closely behind was GloVe, and then came Aravec.