Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Using Burstiness to Improve Clustering of Topics in News Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Emotional reactions to real-world events in social networks
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Microblogging Sentiment Analysis Using Emotional Vector
CGC '12 Proceedings of the 2012 Second International Conference on Cloud and Green Computing
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Microblog has become an increasing valuable resource of up-to-date topics about what is happening in the world. In this paper, we propose a novel approach of detecting real-time events in microblog streams based on bursty sentiments detection. Instead of traditional sentiment orientation like positive, negative and neutral, we use sentiment vector as our sentiment model to abstract subjective messages which are then used to detect bursts and clustered into new events. Experimental evaluations show that our approach could perform effectively for online event detection. Although we worked with Chinese in our research, the technique can be used with any other language.