A New Classificaiton Method for Short Text Based on SLAS and CART

Short text is becoming a more and more popular, we can see it used in many fields like network news, QQ messages, comments in BBS and so on. Because of the fast development of the Internet, more and more people join the Internet, and the data of short text is growing. Most data is useless for us, but other data is still significant for us. Therefore, it is necessary for us to extract the useful short text from the big data and we should do the classification first. However, there are many problems with the short text classification, such as fewer features, irregularity and so on. To solve these problems, we pretreat the short text set first, and then choose the significant features for computing. This paper uses semi-supervised learning method and SVM classifier (SLAS) and classification and regression tree (CART) to improve the traditional methods and it can classify a large number of short texts to mining the useful massage from the short text. The experimental results that got in this paper also show a good improvement.