A New SVM Method for Short Text Classification Based on Semi-Supervised Learning

Short text is a popular text form, which is widely used in short commentary, micro-blog and many other fields. With the development of the social software and movie websites, the size of data is also becoming larger and larger. Most data is useless for us while other data is important for us. Therefore, it is very necessary for us to extract the useful short text from the big data. However, there are some problems such as fewer features, irregularity on the short text classification. To solve the problem we should pretreat the short text set and choose the significant features. This paper use semi-supervised learning and SVM to improve the traditional method and it can classify a large number of short texts to mining the useful massage from the short text. The experimental results also show a good improvement.