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Real-time Event Detection and Tracking in Microblog via Text Chain and Sentiment time series

Microblog becomes one of the most popular media for sharing first-hand information. As more close to real event, detecting and tracking event in microblogs is a research hotspot. To alleviate feature sparsity problem, traditional event detection and tracking methods exploit comments, replies, and reposts to enrich the information of posts. However, ignoring text relationship only leads to more irrelevant information. Aiming at this issue, a novel real-time event detection and tracking method for microblog stream is proposed. For each microblog from the real-time stream, similarity against historical event set is first evaluated. Then, known event microblogs are partitioned by the proposed method sentiment time series, clustered based on word relation graph, and compare with the previous graph to track burst keywords. Meanwhile, text chain is introduced to detect events for unknown event microblogs. Text chain is a unique relationship of microblogs, in which comments, replies, and reposts are used to form a word relation graph of the corresponding post. Posts and attached graphs are clustered to detect new events. Experiments are performed on real-time microblog datasets to evaluate the effectiveness of methods. Results show that the proposed method gains better F-measure than similar methods.