Classification of Indonesian News Articles based on Latent Dirichlet Allocation

Abstract

A massive number of news articles leads to the potential problem in automatic classification task. The discussions on classification of English news articles have been widely studied. However, it is in contrast to automatic classification of Indonesian news articles. The classification method that has been implemented is limited to conventional methods, such as Naïve Bayes and Support Vector Machine. Both methods is rigid in classify a document into one topic. Therefore, we implement one of Topic Modeling methods which represent a document as a distribution of topics and a topic is represented by a set of words. The method is Latent Dirichlet Allocation. The experimental study based on 10-fold cross validation strategy is conducted by employing several parameter includes number of topics (5, 10, and 15) and both LDA’s hyperparameters (0.001, 0.01, and 0.1). The result shows that the best overall accuracy is about 70% for classifying documents of Indonesian news articles into 5 classes, i.e. economic, tourism, criminal, sport, and politics.