A Review on News Article Classification Using Different Machine Learning Algorithms
DOI:
https://doi.org/10.26438/ijcse/v13i7.5863Keywords:
News Article, Classification, Machine Learning, Deep LearningAbstract
In today`s digital landscape, the majority of textual data is unstructured, requiring automated classification techniques for better organization and usability. News article classification into distinct categories such as politics, technology, crime, education, and entertainment is a crucial task in Natural Language Processing (NLP), with applications in recommendation systems, fake news detection, and content personalization. This review paper presents a comprehensive overview of various machine learning approaches employed for multi-label news classification across multiple languages and domains. It synthesizes methodologies, comparative results, and key insights from numerous studies while identifying prevailing challenges, emerging trends, and future research directions. The work primarily focuses on contrasting traditional machine learning algorithms with advanced deep learning and transformer-based models to provide a broad perspective on the evolution of techniques in this domain.
References
[1] T. A. Mahmud, S. Sultana and A. Mondal, “A new technique to classification of bengali news grounded on ml and dl models,” International Journal of Computer Applications, Vol.185, No.18, pp.8887, 2023.
[2] N. Zamzami, H. Himdi and S. F. Sabbeh, “Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques,” Applied Sciences, Vol.13, No.12, pp.70-74, 2023.
[3] I. Rasheed, V. Gupta, H. Banka and C. Kumar, “Urdu Text Classification: A comparative study using machine learning techniques,” in 2018 Thirteenth International Conference on Digital Information Management (ICDIM), Berlin, Germany, 2018.
[4] P. Manas, K. S. Avinash and S. Arpit, “Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers,” in Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025, Vienna, Austria, Association for Computational Linguistics, pp.203-209, 2025.
[5] Y. Sharmin, K. Ratnadip, R. ,. ,. H. R. A, U. Ashraf, Q. ,. ,. U. ,. A. and R. Hasnat, “Multi-category Bangla News Classification using Machine Learning Classifiers and Multi-layer Dense Neural Network,” International Journal of Advanced Computer Science and Applications, Vol.12, 2021.
[6] S. Bihi, K. Amal, E. ,. Bouchra and R. Maryem, “Analyzing BERT’s Performance Compared to Traditional Text Classification Models,” in 25th International Conference on Enterprise Information Systems, 2023.
[7] A. Shreyas and P. P, “Deep Learning-based Text News Classification using Bi-directional LSTM Model,” in Conference: 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), VIT Vellore, 2024.
[8] L. Yongwei, L. Kejun and Z. T. M. Y. Ziyu Liu, “News categorization based on titles with SVM, Naïve Bayesian, random forest, and RNN algorithms,” in 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), Zhuhai, China, 2022.
[9] M. Yuqun, C. Hailong, W. Qing and Z. Xin, “Text classification model based on CNN and BiGRU fusion attention mechanism,” in ITM Web of Conferences, 2022.
[10] A. Jeelani and A. Muqeem, “ONLINE NEWS CLASSIFICATION USING MACHINE LEARNING TECHNIQUES,” IIUM Engineering Journal, Vol.22, No.2, pp.210-225, 2021.
[11] M. H. D. Seyyed, S. ,. Mohammad and M. ,. Morteza, “A novel text mining approach based on TF-IDF and Support Vector Machine for news classification,” in 2016 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, 2016.
[12] N. Beebi, P. C. Nagendra, D. Asutosh, C. S and S. N. Niranjan, “Machine Learning Models for News Article Classification,” in 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, 2023.
[13] D. M. Shweta, “News Classification Using Machine Learning,” International Journal on Recent and Innovation Trends in Computing and Communication, Vol.9, No.5, pp.23-27, 2021.
[14] R. R. Mapitsi and J. S. Tshephisho, “Classification of Crime-related News Titles using Machine Learning Methods,” in 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE), Kuala Lumpur, 2024.
[15] L. Mercy, D. Ranjita and L. Robert, “Mizo News Classification Using Machine Learning Techniques,” in Evolution in Computational Intelligence , 2023.
[16] “Kogilavani, Shanmugavadivel; Malliga, Subramanian; Vasantharan, Kumarakurubaran; G., A., Prethish; S., Sankar;,” in Speech and Language Technologies for Low-Resource Languages. SPELLL 2023., Springer, Cham, pp.255-267, 2024.
[17] Z. Y. Kyaw, M. N. Ye, N. Ye, P. N. Khant and Z. L. Thant, “Implementation of Burmese Language News Classification System by Using SVM and LSTM Machine Learning Algorithm,” in 2023 IEEE 6th International Conference on Computer and Communication Engineering Technology (CCET), Beijing, 2023.
[18] S. Parvathavarthini, M. Shreekanth, K. S. Vignesh and N. S. Santhosh, “News Category Classification using Natural Language Processing Transformer,” in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, 2023.
[19] A. Feroz and G. Shabina, “Linear Support Vector Machine (SVM) with Stochastic Gradient Descent (SGD) training & multinomial Naïve Bayes (NB) in News Classification,” International Journal of Computer Sciences and Engineering, Vol.7, No.4, pp.360-363, 2019.
[20] J. I. K. Beakcheol and W. K. Jong, “Word2vec convolutional neural networks for classification of news articles and tweets,” PLoS ONE , Vol.14, No.8, 2022.
[21] L. Bingyao, C. Jiajing, W. Rui, H. Junming, L. Yuanshuai and W. Jianjun, Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing, 2024.
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