基于词共现和情感元素的突发话题检测算法  

Bursty Topic Detection Based on Word Co-Occurrence and Emotions

作　　者：兰天[1] 郭躬德[2] 

机构地区：[1]福建师范大学数学与计算机科学学院,福州350007 [2]福建师范大学网络安全与密码技术福建省重点实验室,福州350007

出　　处：《计算机系统应用》2016年第8期101-108,共8页Computer Systems & Applications

摘　　要：随着自媒体的迅速发展,微博中的舆情监控和舆情疏导成为一项重大的研究课题.为了解决传统话题检测方法对于微博中大数据的分析往往具有复杂度高、实时性低、影响力小等问题,提出一种基于词共现和情感分析的突发话题检测方法.通过研究微博中情感的突发和共现关系,从而建立情感子空间模型;通过该模型对微博中的信息流进行分类,最后对每个类别中的微博进行主题词提取,实现话题检测的目的.在NLPIR微博内容语料库上的实验结果表明,该方法能够有效地从大规模微博信息中检测突发新闻,提高突发新闻的识别率.With the rapid development of the We-Media, monitoring and guidance of public opinion becomes a significant research subject. Traditional topic detection methods in microblog data analytics encounters the problems of high computational complexity, low real-time and recall rate. An improved algorithm based on emotions and word co-occurrence detection is proposed in this paper aiming at solving these problems. It builds a emotional subspace model through co-occurrence relation of sentiment words in hot events, and classifies the flow of information in weibos. Finally, it gets the aim of topic detection via extracting the subject in the corresponding category. The experimental results carries out on the microblog content corpus of NLPIR and verifies that this method can effectively detect news topic from the massive microblog information and realize the news topic tracking.

关 键 词：话题检测 情感 共现关系 微博 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]