(ACM, Inc., 2015-09) Brand, Dirk; Kroon, Steve; Van der Merwe, Brink; Cleophas, Loek
Accurate classifiers for short texts are valuable assets in
many applications. Especially in online communities, where
users contribute to content in the form of posts and comments, an effective way of automatically categorising posts
proves highly valuable. This paper investigates the use of N-
grams as features for short text classification, and compares
it to manual feature design techniques that have been popu-
lar in this domain. We find that the N-gram representations
greatly outperform manual feature extraction techniques.