Enhancing naive bayes with various smoothing methods for short text classification

Abstract

Partly due to the proliferance of microblog, short texts are becoming prominent. A huge number of short texts are generated every day, which calls for a method that can efficiently accommodate new data to incrementally adjust classification models. Naive Bayes meets such a need. We apply several smoothing models to Naive Bayes for question topic classification, as an example of short text classification, and study their performance. The experimental results on a large real question data show that the smoothing methods are able to significantly improve the question classification performance of Naive Bayes. We also study the effect of training data size, and question length on performance.