Using Social Sensing to Discover Trends in Public Emotion

Abstract

The rapid growth of social media, such as twitter, 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. Detecting the emotions of people on different events are crucial in many applications. However, the high volume and fast pace of social media make it challenging to analyze public emotions from social media data in real-time. In this paper we propose a method to measure public emotion and predict important moments during particular public events. Given a stream of tweets, we analyze the impact of major public events, both tragic and enthusiastic ones, on public emotion. We develop a full-stack architecture that performs real-time emotion analysis on Twitter streams. We design a supervised learning approach for classifying tweets based on the type of the emotion they elicit. Then we aggregate each emotion class to discover emotion-evolving patterns over time. We also propose an online approach to predict emotion-intensive moments during real-life events. Our emotion analysis methodology is shown to present a fast and robust way of analyzing online stream of tweets.