In the Web2.0 era, analyzing user generated opinions has witnessed a booming interest. Among all online messaging forms, the social networking platform is a highly noticed channel. In the last years, there has been a tremendous interest in the area of social networking. From the advertisement point of views, it is quite important to know what users are looking for and are interested in. Thus, identifying user intention becomes a “must have” feature for all social network sites. Personalized and recommendation services are built around interest extraction models. But the outputs of these algorithms are ambiguous in nature. This makes it difficult to understand what users are personally feel and how their intentions propagate through time. By studying both users’ intentions and emotions, simultaneously, one can further explore and investigate the motivation behind these interests. Such findings can be useful to build better models and algorithms that leverage personalized and recommendation services.