For example, NBSVM (Wang and Manning, 2012) uses the ratio of the number of words in positive texts and negative texts to weight words, and achieves competitive results on a range of text classification tasks.
traditional sparse BOW representations take each word or n-gram as a unit and ignore the internal semantics of them.