基于词共现网络的微博话题发现方法

Micro-blog Topic Detection in Frequent Word Networks

微博作为一个重要的信息平台,每天都有大量用户访问,重要的舆论事件在微博上会形成热门话题。本文提出了一种新的微博话题发现方法:基于词共现网络的话题发现方法(Topic detection in freqent word network,TDFWN),来挖掘微博语料中蕴含的热点话题。该方法首先对微博文本中的k频繁词集(k≥3)进行挖掘,利用频繁词集的共现关系构建词共现网络。对该网络进行社区划分,同一社区内的词通常描述同一微博话题,即话题以社区的形式出现。实验结果表明TDFWN算法能够快速、全面地发现微博中的热门话题,并且可以实现微博文本的自动聚类。

As an important information platform,micro blog has a large number of user visits every day,and important public opinion events will form a hot topic on micro blog.In this study,we propose a novel micro blog topic detection method,named TDFWN(Topic detection in frequent word networks),to excavate hot topics in micro blog corpus.First,frequent k-item sets(k≥3)in Microblog text data are mined.Second,a word co occurrence network is build based on these mined frequent k-item sets.Third,the network is partitioned into different communities by using a community detection method,where each community represents a microblog hot topic.At last,the micro-blog text data are clustered into different groups by computing similarity of each micro blog text with the found topics.The empirical study shows that the TDFWN method is able to find hot topics in micro-blog text data and cluster the micro blog text data by the found topics simultaneously.