Tracking the evolution of social emotions with topic models

Many of today’s online news Web sites have enabled users to specify different types of emotions (e.g., angry or shocked) they have after reading news. Compared with traditional 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 challenge 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 evolution 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. Finally, we validate the proposed models with extensive experiments with a real-world data set.