Tracking the Evolution of Social Emotions: A Time-Aware Topic Modeling Perspective

Chen Zhu, Hengshu Zhu, Yong Ge, Enhong Chen, Qi Liu
2014 2014 IEEE International Conference on Data Mining  
Many of today's online news websites have enabled users to specify different types of emotions (e.g., angry and 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
more » ... ces. 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 a model named emotion-Topic over Time (eToT), in which we represent the topics of news as a Beta distribution over time and a multinomial distribution over emotions. While eToT can uncover the latent relationship among news, emotion and time directly, it cannot capture the dynamics of topics. Therefore, we further develop another model named emotionbased Dynamic Topic Model (eDTM), where we explore the state space model for tracking the dynamics of topics. In addition, we demonstrate that both eToT and eDTM 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.
doi:10.1109/icdm.2014.121 dblp:conf/icdm/ZhuZGCL14 fatcat:ot45j6si3jg65pmcjo6ouoitri