Abstract
Vocal media has become a popular method of communication in today's social networks. While conveying semantic information, vocal messages usually also contain abundant emotional information; this emotional information represents a new focus for data mining in social media analytics. This paper proposes a computational method for emotion recognition and affective computing on vocal social media to estimate complex emotion as well as its dynamic changes in a three-dimensional PAD (Position–Arousal–Dominance) space; furthermore, this paper analyzes the propagation characteristics of emotions on the vocal social media site WeChat.
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