Contextual deep learning for accurate news article categorisation with pre-trained embeddings – Scientific Reports

1. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

2. Yang, Z., et al.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1480–1489 (2016)

3. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1 (long and Short Papers), 4171–4186 (2019)

4. Barua, M., Kumar, T., Raj, K. & Roy, A. M. Comparative analysis of deep learning models for stock price prediction in the indian market. FinTech 3(4), 551–568 (2024).

   Google Scholar 

5. Kumar, T., Mileo, A. & Bendechache, M. Saliency-based metric and facekeeporiginalaugment: a novel approach for enhancing fairness and diversity. Multimedia Systems 31(2), 1–14 (2025).

   Google Scholar 

6. Barua, A., Sharif, O. & Hoque, M. M. Multi-class sports news categorization using machine learning techniques: resource creation and evaluation. Procedia Computer Science 193, 112–121 (2021).

   Google Scholar 

7. Saleem, Z., Alhudhaif, A., Qureshi, K. N. & Jeon, G. Context-aware text classification system to improve the quality of text: A detailed investigation and techniques. Concurrency and Computation: Practice and Experience 35(15), 6489 (2021).

   Google Scholar 

8. Petukhova, A. & Fachada, N. Mn-ds: A multilabeled news dataset for news articles hierarchical classification. Data 8(5), 74 (2023).

   Google Scholar 

9. Javed, M., Zhang, Z., Dahri, F. H. & Kumar, T. Enhancing multimodal deepfake detection with local-global feature integration and diffusion models. Signal, Image and Video Processing 19(5), 1–9 (2025).

   Google Scholar 

10. Zhai, Z., Zhang, X., Fang, F. & Yao, L. Text classification of chinese news based on multi-scale cnn and lstm hybrid model. Multimedia Tools and Applications 82(14), 20975–20988 (2023).

    Google Scholar 

11. Umer, M. et al. Impact of convolutional neural network and fasttext embedding on text classification. Multimedia Tools and Applications 82(4), 5569–5585 (2022).

    Google Scholar 

12. Ilie, V.-I., Truică, C.-O., Apostol, E.-S. & Paschke, A. Context-aware misinformation detection: A benchmark of deep learning architectures using word embeddings. IEEE Access 9, 162122–162146 (2021).

    Google Scholar 

13. Liu, C. Long short-term memory (lstm)-based news classification model. Plos one 19(5), 0301835 (2024).

    Google Scholar 

14. Li, X., Han, L., & Jiang, Z.: Deep learning-based news text classification algorithm research. IEEE Access (2024)

15. Tabassoum, N., & Akber, M.A. Interpretability of machine learning algorithms for news category classification using xai. In: 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 770–775 (IEEE, 2024).

16. Garrido-Merchan, E. C., Gozalo-Brizuela, R. & Gonzalez-Carvajal, S. Comparing bert against traditional machine learning models in text classification. Journal of Computational and Cognitive Engineering 2(4), 352–356 (2023).

    Google Scholar 

17. Parvathavarthini, S., et al. News category classification using natural language processing transformer. In: 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), 1185–1189 (IEEE, 2023). 

18. Guo, S. & Yao, N. Polyseme-aware vector representation for text classification. IEEE Access 8, 135686–135699 (2020).

    Google Scholar 

19. Mao, K. et al. A text classification model via multi-level semantic features. Symmetry 14(9), 1938 (2020).

    Google Scholar 

20. Sakor, A., Singh, K. & Vidal, M.-E. Resorting to context-aware background knowledge for unveiling semantically related social media posts. IEEE Access 10, 115351–115371 (2022).

    Google Scholar 

21. Swati, S., Mladenić, D., & Grobelnik, M. An inferential commonsense-driven framework for predicting political bias in news headlines. IEEE Access (2023)

22. Khudhair, I. Y., Majeed, S. H., Ahmed, A. M. S., Alsaeedi, M. A. K. & Aswad, F. M. An improved hybrid gru and cnn models for news text classification. JOIV: International Journal on Informatics Visualization 9(1), 303–313 (2025).

    Google Scholar 

23. Zhu, S., & He, C. Chinese news classification based on ernie and attention fusion features. In: Proceedings of the 2023 6th International Conference on Robot Systems and Applications, 134–138 (2023)

24. Chawla, S., Kaur, R. & Aggarwal, P. Text classification framework for short text based on tfidf-fasttext. Multimedia Tools and Applications 82(26), 40167–40180 (2023).

    Google Scholar 

25. Misra, R.: News category dataset. arXiv preprint arXiv:2209.11429 (2022)

26. Zhang, X., Zhao, J., & LeCun, Y. Character-level convolutional networks for text classification. Advances in neural information processing systems 28, (2015)

27. Wang, H. & Li, X. Chinese news text classification based on convolutional neural network. Journal on Big Data 4(1), 41 (2022).

    Google Scholar 

28. Kumar, D., AbuHashem, Y.A., & Durumeric, Z. Watch your language: Investigating content moderation with large language models. In: Proceedings of the International AAAI Conference on Web and Social Media, 18, 865–878 (2024)

29. Vavekanand, R., Das, B. & Kumar, T. Daugsindhi: a data augmentation approach for enhancing sindhi language text classification. Discover Data 3(1), 22 (2025).

    Google Scholar 

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