Abstract
Conversational partners influence each others' emotions and topics. Using a large dataset of Twitter conversations and an unsupervised machine learning technique, we discover patterns of emotion influence in naturally occurring conversations. We describe our computational framework for automatically classifying emotions, analyzing the emotional transitions, and discovering emotion influence patterns. We found that conversational partners usually express the same emotion (emotion contagion), but when they do not, one of the conversational partners tends to respond with a positive emotion. Also, tweets containing sympathy, apology, and complaint are significant emotion influencers. One of the interesting findings is that expressing a desired emotion is the best strategy to alter partner's emotion.
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