Machine Learning Models for News Article Classification

An enormous amount of data is produced these days on a daily basis. News articles are one of the major parts of the daily data, which people refer to and most of the data are unstructured or unorganized so a better strategy and tools that can enable a better management processing data iteration should be adopted. Categorization is one of the methods to organize this unstructured data into structured data. Article Classification is a need for structuring these online articles. Knowledge should be categorized into a useful set of groups because it is important and necessary. Automatic text classification is desperately needed to categorize these growing numbers of machine-readable data. Using text labelling, a supervised learning technique, unlabeled data is divided into predetermined groups of labelled data. In this paper, five different machine learning models are used to classify news articles into four classes. The best accuracy was achieved by the SVM (support vector machine) model, with a value of 89.35%.