一种基于情感符号的在线突发事件检测方法

摘要

How to effectively and efficiently detect the online public events in massive data streams has become a hot research area nowadays.In this paper,we propose a novel approach to mine online events based on emoticons.Emoticons in texts streams always burst with hot events,so we could monitor the states of emoticons and quickly mine the bursty periods so as to detect events.Firstly,we build an emoticon model based on frequent patterns mining and mutual information,and detect their periods using Kleinberg's method.Then,we use Heuristic Affinity Propagation (HAP) to cluster and aggregate events.Besides,a recycle module is proposed in the last part of the frame so as to make precise event abstraction.Experimental results show that our algorithm can detect online events in microblog streams effectively,and could meet the needs of real-time process both in speed and accuracy.%如何快速高效检测出海量数据流中的突发事件是目前的研究热点之一.文中针对微博数据流,提出了一种新颖的基于情感符号的在线突发事件检测算法框架.伴随着事件的发生,文本流中情感符号也存在突发现象.文中通过实时监测情感符号变化态势,及时发现情感符号的突发期,达到挖掘突发事件的目的.首先基于频繁模式挖掘和互信息相结合的算法构建情感符号模型,并通过此模型抽取数据流中的情感符号,采用改进Kleinberg算法检测突发期,通过启发式的近邻传播聚类算法检测突发事件并对事件进行合并.同时,算法设置了离线回收机制,对不含情感符号的博文进行回收利用以保证事件概要抽取的完备性.实验表明,该算法可有效地挖掘出突发事件,无论在速度还是精度上都能保证实时在线处理的要求.