IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 179 10.51583/IJLTEMAS.2026.150700177 Machine Learning Classifying News Articles on Nigerian Newspapers Using Machine Learning Stephenson C. Echezona Department of Computer Science, University of Nigeria, Nsukka. Nigeria Blessing Cimezie Uzo Department of Computer Science, University of Nigeria, Nsukka. Nigeria Deborah Ogoamaechi Chukwu Department of Computer Science, University of Nigeria, Nsukka. Nigeria Celestine Ugwu Department of Computer Science, University of Nigeria, Nsukka. Nigeria George E Okereke Department of Computer Science, University of Nigeria, Nsukka. Nigeria Izu Uzochukwu Department of Computer Science, University of Nigeria, Nsukka. Nigeria Uchenna E. Omeh Department of Computer Science, University of Nigeria, Nsukka. Nigeria 15 7 2412 2424 14 08 2026 19 08 2026 27 08 2026 News sources newspaper articles classify news articles Personalized recommendation Nigerian News articles Abstract

With the escalation of news sources, it presents challenge for a reviewer to find the exact newspaper article(s) that would meet ones need(s); hence, the need to classify news articles so that users can efficiently get the information they need with minimal efforts. The classification of news presents many advantages, such as, determining which newspaper to look out for albeit the date of publication, as well as providing the user with personalized recommendations based on the user's previous areas of interest. Also, for research purposes, it can tell the class of news a particular string belongs to, in addition to finding trending news articles. Studies on classification of news for other countries’ Newspapers have been ongoing, into a variety of groups/categories. Examples are: Arabic, Tigrigna, Uzbek, China, and Thai news headlines have all been classified into a variety of categories. These would help visitors to quickly find the news stories that interest them, little to no research has been done to categorize Nigerian news articles. Therefore, this research proposed the classification of Nigerian news articles into five different categories; such as, sports, entertainment, business, health, and politics using machine learning - to the extent of displaying the class of a string keyed in by the user - as a means of filling-in the gap in the existing body of literature. A local source was used to compile a dataset consisting of five different Nigerian news articles. Feature extraction employed Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM). These two algorithms were used to train the model. The research will also evaluate the outcomes of several categorization algorithms and compare the efficacy of these algorithms according to some of performance metrics including accuracy, precision, and recall.

 References Ahmed J. and Muqeem A. (2021) Online News Classification Using Machine Learning Techniques. Alaa Tharwat, Tarek Gaberc, Abdelhameed Ibrahim, and Aboul Ella Hassaniene, ( 2017), Linear discriminant analysis: A detailed Tutorial a Department of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt Main, Germany, A research paper, AI Communications00 (20xx) 1–22 1 DOI 10.3233/AIC-170729 IOS Press, pp1 – 22 Alexander Berkovich, (2025), Understanding Model Evaluation Metrics for Image Classification, 175 S San Antonio Rd SUITE 102, Los Altos, CA 94022, United States, https://akridata.ai/blog/understanding-model-evaluation-metrics-for-image-classification/ Babalola, E.A. (2002). Newspapers as instruments for building literate communities: The Nigerian experience. Nordic Journal of African Studies 11(3), 403-410. Bahiyah O. and Oberiri D. A. (2020) Newspaper Readership Pattern Among Nigerian University Students: Perspectives from Mass Communication Students; Library Philosophy and Practice (e-journal) Dadgar S. M. H., Araghi M. S., and Farahani M. M. (2016) “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, India, Mar. 2016, pp. 112–116, doi: 1109/ICETECH.2016.7569223. Egwom, O.J., Hassan, M., Tanimu J.J., Hamada, M., Ogar, O.M. (2022) an LDA–SVM Machine Learning Model for Breast Cancer Classification. Biomedinformatics 2022,2, 345–358. https://doi.org/10.3390/biomedinformatics2030022 Fesseha A., Xiong S,. Emiru E. D., Dahou A. (2020) Text classification of news articles using machine learning on low-resourced language: Tigrigna. In Proceedings of the 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, pp 34-38. https://doi.org/10.1109/ICAIBD49809.2020.9137443. Hartmann J., Huppertz J., Schamp C., and Heitmann M., (2019) “Comparing automated text classification methods,” Int. J. Res. Mark., vol. 36, no. 1, pp. 20–38, 2019. Huang C. M, Jiang Y. J. (2019) An empirical study on the classification of Chinese news articles by machine learning and deep learning techniques. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC), Kobe, Japan, pp 1-6. https://doi.org/10.1109/icmlc48188.2019.8949309 Huang M. W., Chen, C. W., Lin, W. C., Ke, S. W., & Tsai, C. F. (2017). SVM and SVM ensembles in breast cancer prediction. PLoS ONE, 12(1), 1–14. https://doi.org/10.1371/journal.pone.0161501 Jang B., Kim I., Kim J. W. (2019) Word2vec convolutional neural networks for classification of news articles and tweets. PLOS ONE, 14(8): e0220976. https://doi.org/10.1371/journal.pone.0220976 Miao F., Zhang P., Jin L., and Wu H. (2018) “Chinese News Text Classification Based on Machine Learning Algorithm,” in 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, Aug. 2018, pp. 48–51, doi: 10.1109/IHMSC.2018.10117. Nigerian experience. Nordic Journal of African Studies 11(3), 403-410. Noppakaow A, Uchida O. (2019) Examinations on the Performance of Classification Models for Thai News Articles. In Proceedings of the 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE), Pattaya, Thailand, 2019, pp. 1-4 https://doi.org/10.1109/iciteed.2019.8929959 Qadi L. A, Rifai H. E, Obaid S., Elnagar A. (2019) Arabic text classification of news articles using classical supervised classifiers. In Proceedings of the 2nd International Conference on new Trends in Computing Sciences (ICTCS), Amman, Jordan, pp 1-6. https://doi.org/10.1109/ICTCS.2019.8923073. Rabbimov I. M, Kobilov S. S. (2020) Multi-class text classification of Uzbek news articles using machine learning. Journal of Physics: Conference Series, 1546(1): 012-097. Rajbharath R., and Sankari L. (2017). Predicting Breast Cancer using Random Forest and Logistic Regression. 7(4), 10708–10713 Winster S. G, Kumar M. N. (2020) Automatic classification of emotions in news articles through ensemble decision tree classification techniques. Journal of Ambient Intelligence and Humanized Computing, 1–12. https://doi.org/10.1007/s12652-020-02373-5