Events Detection and Temporal Analysis in Social Media

被引：1

作者：

Jia, Yawei ^{[1
]}

Xu, Jing ^{[1
]}

Xu, Zhonghu ^{[1
]}

Xing, Kai ^{[1
]}

机构：

[1] Univ Sci & Technol China, 443 Huangshan Rd, Hefei 230027, Anhui, Peoples R China

来源：

NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016) | 2016年 / 10102卷

关键词：

Event detection; KeyGraph; Co-occurrence; Temporal analysis; TOPIC DETECTION;

D O I：

10.1007/978-3-319-50496-4_33

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the past few years, event detection has drawn a lot of attention. We proposed an efficient method to detect event in this paper. An event is defined as a set of descriptive, collocated keywords in this paper. Intuitively, documents that describe the same event will contain similar sets of keywords. Individual events will form clusters in the graph of keywords for a document collection. We built a network of keywords based on their co-occurrence in documents. We proposed an efficient method which create a keywords weight directed graph named KeyGraph and use community detection method to discover events. Clump of keywords describing an event can be used to analyse the trend of the event. The accuracy of detecting events is over eighty percents with our method.

引用

页码：401 / 412

页数：12

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