We present a system that automatically discovers important events that have significantly driven people’s sentiment changes towards a target using Twitter data (i.e. tweets). This system can also provide the time, importance, and description of events that are associated with people’s sentiment changes. In this system, a sentiment classifier is used as the sensor to detect the time points of those changes. It is also used as the filter to effectively eliminate a considerable amount of noisy information and select the most informative tweets to be further analyzed for event descriptions. Discovered events are described from the following aspects, 1) the most important tweets ranked by tweet-based TextRank algorithm, 2) the topics generated by the nonnegative matrix factorization, and 3) the most important keywords generated by word-based TextRank algorithm. Compared with traditional event discovery techniques, the experimental results show that this system can effectively discover important patterns from tweets and unveil 3Ws of an event (i.e. what happens, when it happens, what its effect is), which provides good reference on understanding behavior changes and making strategies. Furthermore, the system was applied to analyze people’s sentiment changes towards the two candidates during the 2016 U.S. presidential election. It can also be applied in other scenarios where people’s attitude plays an important role like the brand influence marketing and financial investment markets
