With the rapid development of social networks, electronic commerce, mobile Internet and other technologies, all kinds of Web data expand rapidly. There are a large number of emotional texts on the Internet, and they are very helpful to understand the netizen’s opinion and viewpoint if fully explored. The aim of emotion classification is to predict the emotion categories of emotive texts, which is the core of emotion analysis. In this paper, we first introduce the background knowledge of emotion analysis including different emotion classification systems and its application scenarios on public opinion management and control, business decisions, opinion search, information prediction, emotion management. Then we summarize the mainstream approaches of emotion classification, and make a detailed description and analysis on these approaches. Finally, we expound the problems of data sparsity, class imbalance learning, dependence for the strong domain knowledge and language imbalance existing in the emotion analysis work. The research progress of text emotion analysis is summarized and prospect combined with large data processing, the mixing of multiple media, deep learning development, mining on a specific topic and multilingual synergy.