Text classification algorithm based on SLAS-C

Abstract

Nowadays, mobile marketing is becoming increasingly important both strategically and economically because of the mobile devices. Short text is becoming a popular text form which can be seen in many fields such as network news, QQ messages, comments in BBS and so forth. Besides, our mobile devices also contain a lot of data of short text. To extract useful information from the short text more efficiently, this paper proposes SLAS (semi-supervised learning method and SVM classifier) and CART (classification and regression tree) to improve the traditional methods, which can classify massive short texts to mining the useful information from the short texts. The experiment also shows a better result than before, which has a more than 10% increase, including precision rate, recall rate and F1 value, besides, the running time is reduced by half than the KNN algorithm.