News Topic Classification Using Machine Learning Techniques Pramod Sunagar, Anita Kanavalli, Sushmitha S. Nayak, Shriya Raj Mahan, Saurabh Prasad, and Shiv Prasad Abstract News topic classification is a method of classifying news articles avail- able in text data into some predefined classes or labels. This is one of the appli- cations of text classification. Text classification can be applied in the fields of spam filtering, language recognition, segmenting customer feedbacks, segregating technical documents, etc. This paper discusses news topic classification on AG’s News Topic Classification Dataset using machine learning algorithms such as linear support vector machine, multinomial Naive Bayesian classifier, K-Nearest Neighbor, Rocchio, bagging, and boosting. This paper discusses three steps for classifica- tion, namely pre-processing of text, then applying feature extraction techniques, and finally implementing machine learning algorithms. These algorithms are compared using evaluation metrics like Accuracy, Recall, Precision, and F1 Score. Keywords Text Classification · Natural language processing (NLPs) · Term frequency–inverse document Frequency (TF-IDF) · Support vector machine (SVM) · K-nearest neighbours (KNN) · Naïve Bayes · Rocchio P. Sunagar (B ) · A. Kanavalli · S. S. Nayak · S. R. Mahan · S. Prasad · S. Prasad Department of Computer Science & Engineering, Ramaiah Institute of Technology, Bangalore 560054, India e-mail: pramods@msrit.edu Visvesvaraya Technological University, Belagavi, Karnataka, India A. Kanavalli e-mail: anithak@msrit.edu S. S. Nayak e-mail: nayaksushmitha90@gmail.com S. R. Mahan e-mail: shriyarajmahan@gmail.com S. Prasad e-mail: saurabhprasad12@gmail.com S. Prasad e-mail: shivpsmy227721@gmail.com © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. Bindhu et al. (eds.), International Conference on Communication, Computing and Electronics Systems, Lecture Notes in Electrical Engineering 733, https://doi.org/10.1007/978-981-33-4909-4_35 461