Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like to express their emotion in OSNs, which provides wonderful opportunities to gain insight into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we can classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. By analyzing the collective emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence for that human emotion can be spread from one person to another. Furthermore, we find that the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self-evolving process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self-evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network by selecting seed users according to k-core rather than degree. Emotion is a fundamental object of human existence and determined by a complex set of factors. With the rapid development of online social networks (OSNs), more and more people would like express their emotion in OSNs, which provides wonderful opportunities to gain insight into into how and why individual emotion is evolved in social network. In this paper, we focus on emotion dynamics in OSNs, and try to recognize the evolving process of collective emotions. As a basis of this research, we first construct a corpus and build an emotion classifier based on Bayes theory, and some effective strategies (entropy and salience) are introduced to improve the performance of our classifier, with which we we classify any Chinese tweet into a particular emotion with an accuracy as high as 82%. emotions in our sample networks in detail, we get some interesting findings, including a phenomenon of emotion synchronization between friends in OSNs, which offers good evidence f or that human emotion can be spread from one person to another. we, that find the the number of friends has strong correlation with individual emotion. Based on those useful findings, we present a dynamic evolution model of collective emotions, in which both self- evirit process and mutual-evolving process are considered. To this end, extensive simulations on both real and artificial networks have been done to estimate the parameters of our emotion dynamic model, and we find that mutual-evolution plays a more important role than self- evolution in the distribution of collective emotions. As an application of our emotion dynamic model, we design an efficient strategy to control the collective emotions of the whole network byorus seed users according to k-core rather than degree.