A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification

Abstract

In this era, a rapid thriving Internet occasionally complicates users to retrieve news category furthermore if there are plentiful of news to be categorized. News categorization is a technique can be used to retrieve a category of news which gives easiness for users. Internet has vast amounts of information especially at news. Therefore, accurate and speedy access is becoming ever more difficult. This paper compares a news categorization using k -Nearest Neighbor, Naive Bayes and Support Vector Machine. Using vary of variables and through a several steps of preprocessing which proving k-Nearest Neighbor is producing a capable accuracy competes with Support Vector Machine whereas Naive Bayes producing just an average result, not as good as k -Nearest Neighbor and Support Vector Machine yet as bad as k -Nearest Neighbor and Support Vector Machine ever reach. As the results, k -Nearest Neighbor using correlation measurement type produces the best result of this experiment.