News Categorization based on Titles with SVM, Naïve Bayesian, Random Forest, and RNN algorithms

News categorization, a text classification task, is now commonly used in many news websites. However, many of these news classifiers require full content of the news, which would cost great amounts of time for computation. In this paper, we focus on the possibility of categorizing news by its title with Support Vector Machines, Random Forest Classifiers, Naive Bayes, and Recurrent Neural Network. First, we explore some widely used pre-processing methods, including Bag of Words and Word2Vec. Then we combine these different pre-processing methods with the machine learning algorithms mentioned above to create different models. We measure their performances on the News Aggregator Data Set from UCI Machine Learning Repository, which contains over 400,000 pieces of news over 4 main categories. To evaluate the related performances, we use 85% data as a training set and 5% data as a validation set, and finally, use 10% data as a testing set. Comprehensive experimental results demonstrate that even with only the news titles, some models can still perform well in this challenging task. Therefore, it is possible to categorize news through its title in high accuracy yet with a much lower computing cost compared to full-text classification.