Real-time Public Mood Tracking of Chinese Microblog Streams with Complex Event Processing

There are not many real-time public mood tracking frameworks over social media streams at present. Real-time public mood tracking over microblogs becomes necessary for further studies with low-latency requirements. To address this issue, we propose a hierarchical framework for real-time public mood time series tracking over Chinese microblog streams using complex event processing. Complex event processing is able to handle high-speed and high-volume data streams. First, we transform microblogs into emotional microblog events through the text sentiment analysis. Then, we apply an online batch window technique to summarize the public mood in different periods. For the public mood time series, we use smoothing and trend following methods to find the rising or falling trends of the public mood. Finally, we apply the method to 6606 microblogs to verify its feasibility. The result demonstrates that the proposed model is not only feasible but also effective.