User-Level Sentiment Evolution Analysis in Microblog

People's attitudes towards public events or products may change overtime, rather than staying on the same state. Understanding how sentiments change overtime is an interesting and important problem with many applications. Given a certain public event or product, a user's sentiments expressed in microblog stream can be regarded as a vector. In this paper, we define a novel problem of sentiment evolution analysis, and develop a simple yet effective method to detect sentiment evolution in user-level for public events. We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments. Based on this model, we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence. Moreover, we develop an improve Affinity Propagation algorithm to detect why people change their sentiments. Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.