A Comparative Analysis of Machine Learning Models in News Categorization
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Abstract
The constant stream of news nowadays highlights the necessity for meticulous assessment to ensure that the information accurately reaches its intended audience with the least amount of delay least delay. Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline.
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