A Comparative Study on Vietnamese Text Classification Methods

Abstract

Text classification concerns the problem of automatically assigning given text passages (or documents) into predefined categories (or topics). Whereas a wide range of methods have been applied to English text classification, relatively few studies have been done on Vietnamese text classification. Based on a Vietnamese news corpus, we present two different approaches for the Vietnamese text classification problem. By using the Bag Of Words - BOW and Statistical N-Gram Language Modeling - N-Gram approaches we were able to evaluate these two widely used classification approaches for our task and showed that these approaches could achieve an average of >95% accuracy with an average 79 minutes classifying time for about 14,000 documents (3 docs/sec). Additionally, we also analyze the advantages and disadvantages of each approach to find out the best method in specific circumstances.