IEEE TRANSACTIONS ON AFFECTIVE （IEEE情感）.pdfVIP

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. X, NO. X, JANUARY 20XX 1 Quantitative Study of Individual Emotional States in Social Networks Jie Tang, Yuan Zhang, Jimeng Sun, Jinghai Rao, Wenjing Yu, Yiran Chen, and ACM Fong Abstract—Marketing strategies without emotion will not work. Emotion stimulates the mind 3000 times quicker than rational thought. Such emotion invokes either a positive or a negative response and physical expressions. Understanding the underlying dynamics of users’ emotions can efficiently help companies formulate marketing strategies and support after-sale services. While prior work has focused mainly on qualitative aspects, in this paper, we present our research on quantitative analysis of how an individual’s emotional state can be inferred from her historic emotion log and how this person’s emotional state influences (or is influenced by) her friends in the social network. We statistically study the dynamics of individual’s emotions and discover several interesting as well as important patterns. Based on this discovery, we propose an approach referred to as MoodCast to learn to infer individuals’ emotional states. In both mobile-based social network and online virtual network we verify the effectiveness of our proposed approach. Index Terms—Social network, Predictive model, Emotion dynamics, Social influence 1 INTRODUCTION of an individual’s emotional state can influence others Emotion can be defined as a complex series of psy- in positive or negative ways and the spread of emotion chophysical stimulate that arises spontaneously 3000 may further trigger large cascade adopt