Hybrid Lexical–Contextual Learning for Arabic News Classification: Integrating TF–IDF with AraBERT Embeddings

Abstract

Arabic text classification is still a daunting undertaking because of the rich morphology, derivational complexity as well as lexical variability that is inherent in the language. Though transformer-based pretrained models have been a major breakthrough in Arabic Natural Language Processing (NLP), recent data indicates that conventional lexical representations continue to give a good discriminating ability in structured tasks like news classification. The given study will be a comparative analysis of classical machine learning models, transformer-based fine-tuning, and lexical contextual fusion framework as a hybrid method in Arabic multi-class news classification. A subset of the MAAD dataset (13,866 Arabic news articles) in six categories was carefully selected and cleaned and sampled to conduct experiments on a balanced and well-cleaned subset of the entire data set. To guarantee the data quality, the Arabic-character ratio filtering and length constraints were used to eliminate the corrupted and non-Arabic samples. TF-IDF features with Logistic regression and Linear Support Vector machine (SVM) were used to implement baseline models and contextual modeling was done using fine-tuning AraBERTv2. Moreover, the hybrid feature-level fusion model was suggested by adding TFIDF vectors with contextual embeddings were obtained using AraBERT. Experimental findings indicate that the traditional linear models are still very competitive whereby Linear SVM has an accuracy of 95.57 percent. The fine-tuned AraBERT obtained an accuracy of 91.6%, which puts lexical features in structured news datasets in the spotlight as still significant. The hybrid model with the highest performance in the proposed structure was 96.03% accurate and 96.02% Macro-F1 score, which shows that combining lexical statistical cues and contextual embeddings is complementary. These results show that hybrid lexical-contextual representations offer a powerful, computationally effective solution to Arabic news classification. The analysis offers reproducible experimental environments and intricate statistical investigation, to add the empirical data regarding the interaction between classical and deep methods of learning Arabic NLP.