Tibetan Microblog Emotional Analysis Based on Sequential Model in Online Social Platforms.

With the development of microblogs, selling and buying appear in online social platforms such as Sina Weibo and Wechat. Besides Mandarin, Tibetan language is also used to describe products and customers' opinions. In this paper, we are interested in analyzing the emotions of Tibetan microblogs, which are helpful to understand opinions and product reviews for Tibetan customers. It is challenging since existing studies paid little attention to Tibetan language. Our key idea is to express Tibetan microblogs as vectors and then classify them. To express microblogs more fully, we select two kinds of features, which are sequential features and semantic features. In addition, our experimental results on the Sina Weibo dataset clearly demonstrate the effectiveness of feature selection and the efficiency of our classification method.