Issue No. 02 - April-June (2012 vol. 3)
Jinhai Rao , Nokia Res. Center, Beijing Econ. & Technol. Dev. Area, Beijing, China
Jimeng Sun , IBM TJ Watson Res. Center, Hawthorne, NY, USA
Yuan Zhang , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Jie Tang , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Wenjing Yu , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
Yiran Chen , Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
A. C. M. Fong , Sch. of Comput. & Math. Sci., Auckland Univ. of Technol., Auckland, New Zealand
Marketing strategies without emotion will not work. Emotion stimulates the mind 3,000 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.
statistical analysis, behavioural sciences computing, marketing data processing, mobile computing, social networking (online), social influence, quantitative study, individual emotional states, marketing strategies, positive response, negative response, physical expressions, user emotion dynamics, companies, after-sale services, historic emotion log, statistical analysis, MoodCast, mobile-based social network, online virtual network, Social network services, Correlation, Mobile communication, Mobile computing, Predictive models, Mood, IEEE Transactions on Affective Computing, social influence., Social network, predictive model, emotion dynamics
Jinhai Rao et al., "Quantitative Study of Individual Emotional States in Social Networks," in IEEE Transactions on Affective Computing, vol. 3, no. , pp. 132-144, 2012.