Quantitative Study of Individual Emotional States in Social Networks


Selling ways while not emotion can not work. Emotion stimulates the mind three,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 facilitate corporations formulate selling strategies and support when-sale services. While previous work has targeted mainly on qualitative aspects, in this paper we gift our analysis on quantitative analysis of how a private's emotional state can be inferred from her historic emotion log and the way this person's emotional state influences (or is influenced by) her friends within the social network. We statistically study the dynamics of individual's emotions and find out many fascinating as well as necessary patterns. Based on this discovery, we propose an approach referred to as MoodCast to be told to infer individuals' emotional states. In both mobile-primarily based social network and on-line virtual network, we tend to verify the effectiveness of our proposed approach.

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