Discover trends in public emotion using social sensing.

Social networks, such as Twitter and Facebook, are increasingly used by individuals to share their opinions and feelings on current issues and events in the form of text messages. This results in massive amounts of text stream data rich with emotional content. Such data provides a great opportunity for identifying and analyzing people's emotions in response to various public events, such as epidemics, terrorist attacks and political elections. However, the high volume and fast pace of social data make it challenging to analyze public emotions in social networks in real-time. In this paper we propose an online method to measure public emotion and detect emotion-intensive moments during real-life events. We first classify emotions expressed in text stream messages using a supervised learning approach. Then we aggregate each emotion class to discover emotionevolving patterns over time and detect emotion-intensive moments. Our emotion analysis method is shown to present a fast and robust approach of analyzing online streams of text messages.