Arabic News Text Classification Using Deep Learning Models with Dynamic N-grams

Abstract

The complexity and morphological richness of the Arabic language pose significant challenges in natural language processing (NLP), including issues with contextual understanding and feature extraction. Traditional deep learning architectures such as CNNs, LSTMs, and GRUs often struggle to effectively model these linguistic intricacies, limiting their performance on Arabic text analysis tasks. To address these limitations, the study integrates parallel multi-kernel word-level convolutional features into conventional and hybrid deep learning models. The convolutional windows capture short contextual relationships among neighboring word tokens, while recurrent components model longer sequential evidence; the framework does not directly analyze roots, affixes, or other within-word morphological structures. These enhancements are integrated into CNN, LSTM, GRU, and hybrid architectures such as LSTM-CNN and GRU-CNN. A comprehensive evaluation was conducted across varying learning rates to assess the impact of the enhanced configurations on model performance. The results indicate competitive performance within the evaluated architectures and dataset variants, although the magnitude of improvement depends on the model and learning rate. Under their best settings, the Dynamic N-gram LSTM-CNN achieved an accuracy of 93.32%, while the Dynamic N-gram LSTM achieved 93.57%. Because previously published studies use different corpora, class configurations, preprocessing pipelines, and evaluation protocols, these results are not presented as evidence of state-of-the-art superiority. Instead, the study provides a systematic within-study assessment of model sensitivity to architecture, preprocessing, and learning-rate selection. Future directions include character- and subword-level modeling, transformer-based architectures, and domain-specific tasks such as sentiment analysis and information retrieval.