The VSM benefits from weighing keywords in different topics by vectors in order to discover unique words and leave out less distinctive ones in each topic, thereby outperforming the Naïve Bayes classifier.
since VSM considers similarity between two words as a cosine function with independent dimensions, it is difficult to represent the relations among many words.