Multi-Class News Classification with BERT, DistilBERT, RoBERTa, and ELECTRA Natural Language Processing Models

The rapid proliferation of digital news sources today necessitates the effective analysis and classification of large-scale textual data. In this study, BERT (Bidirectional Encoder Representations from Transformers) and its derivatives — DistilBERT, RoBERTa, and ELECTRA — were comparatively evaluated for the automatic classification of multi-class news texts. Each model performed the classification task by learning the contextual and semantic features of texts belonging to different news categories. The models’ performances were analyzed based on various metrics such as accuracy, precision, recall, and F1 score. Among them, the DistilBERT model demonstrated the best performance, achieving an accuracy of 0.92 and a mean F1 score of 0.92. The findings reveal that transformer-based models exhibit strong performance in news classification tasks and further illustrate the impact of architectural differences among these models on classification success. Accordingly, important insights have been gained regarding the practical effectiveness of different language model architectures.