Chinese SNS blog classification using semantic similarity

Social Network Services have become an important medium for people to communicate ideas and share interests in recent years. Blogs published and shared by users in this virtual world are one of the main sources of user-generated information. Classifying these freestyle blogs can help understand user interests and assist applications such as search and marketing. In this paper, we propose a new method of multi-label classification for Chinese blogs. By applying Dempster-Shafer theory on semantic word similarity algorithms, we achieve automatic classification without use of difficult-to-obtain training sets. Experiments were conducted on real world data from RENREN.com, the biggest SNS (Social Network Services) in China. Results show that the proposed method achieves satisfactory performance in multi-labeling real world SNS blogs as well as corpus.