A Topic Modeling Perspective For Analysis Of Social Emotions

Many of today’s online news websites have enabled users to specify different types of emotions (e.g., angry or shocked) they have after reading news. Compared with tradi-tional user feedbacks such as comments and ratings, these specific emotion annotations are more accurate for expressing users’personal emotions. In this paper, we propose to exploit these users’emotion annotations for online news in order to track the evolution of emotions, which plays an important role in various online services. A critical chal-lenge is how to model emotions with respect to time spans. To this end, we propose a time-aware topic modeling perspective for solving this problem. Specifically, we first develop two models named emotion-Topic over Time (eToT) and mixed emotion-Topic over Time (meToT), in which the topics of news are represented as a Beta distribution over time and a multinomial distribution over emotions. While they can uncover the latent relationship among news, emotion and time directly, they cannot capture the evo-lution of topics. Therefore, we further develop another model named emotion-based Dynamic Topic Model (eDTM), where we explore the state space model for tracking the evolution of topics. In addition, we demonstrate that all of proposed models could enable several potential applications, such as emotion prediction, emotion-based news recommendations and emotion anomaly detections. The major work and contributions are as follows:· First, we provide a comprehensive study to track the evolution of social emotions by exploiting the user emotion annotations from online news. Indeed, this study is vital for the successful development of various social services.· Second, we propose three novel topic models, i.e., eToT, meToT, and eDTM, for solving the problem of tracking the evolution of social emotion. Particularly, the proposed models can effectively model the emotions, news and time from differ-ent views. Also, we introduce some novel emotion-based applications enabled by the proposed models.· Third, we carry out extensive experiments with a real-world data set which was collected from Sina News to evaluate the proposed models. As shown in our experiments, the proposed models are all effective for modeling social emotions and other enabled applications.