Social network users clustering based on multivariate time series of emotional behavior

It is known that the social network is an excellent source for gathering the emotions of people. There are thousands of micro-blogs posted in every second and every micro-blog that may contain a variety of user's emotions. The users' collective emotional behaviors are with great impacts on today's societies, so it is good to find groups for society management based on users' emotional behavior. This article focuses on analyzing multivariate emotional behavior of users in social network and the goal is to cluster the users from a fully new perspective-emotions. The following tasks are completed: firstly, the multivariate emotion of Chinese micro-blog with vector is analyzed, and multivariate time series to describe the user's emotional behavior are constructed. Seconedly, considering principal component analysis (PCA) in similarity and distance similarity, the similarity of the multivariate emotion time series is measured. The contribution could be summarized as follows: groups of users though different emotions in social network are discovered. The emotional fluctuation and intensity of users are considered as well. Experiment in clustering effectively illustrates the emotional behavior characteristics of the users in different groups.