标题:A framework for detecting key topics in social networks
作者:Han, Peng ;Zhou, Ning
通讯作者:Zhou, Ning
作者机构:[Han, Peng ] China Communications Construction Group Design Institute Co.,Ltd, Beijing; 100079, China;[Zhou, Ning ] National Digital Switching System 更多
会议名称:2nd International Conference on Big Data Technologies, ICBDT 2019
会议日期:August 28, 2019 - August 30, 2019
来源:ACM International Conference Proceeding Series
出版年:2019
页码:235-239
DOI:10.1145/3358528.3358540
摘要:Finding the key topics in a large amount of short texts in social networks is a hot research point in data mining. There are a lot of models and algorithms to solve this problem, but they are not designed for social networks where many new words and nonstandard writings, grammars exist. It's more difficult to detect key topics in social networks because of these characteristics. In this paper, we propose a framework for detecting key topics in social networks. First, we get the posts in social networks using a focused crawler. Then we introduce the Word Segment Merging (WSM) method to identify new phrases in short texts and represent a document with the vector space model (VSM). At last, we model the life cycle of topics for clustering and popularity computing. Experiments on three datasets of SINA Weibo show that our method is better than existing state-of-arts models.
© 2019 Association for Computing Machinery.
收录类别:EI
资源类型:会议论文
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