基于词相关性特征的多归属谱聚类突发事件检测

doi:10.11959/j.issn.1000-436X.2020215

Abstract

Abstract:

For current methods for extracting emergencies had problems of low accuracy and low efficiency, an emergency detection method based on the characteristics of word correlation was proposed, which could quickly detect emergency events from the social network data stream, so that relevant decision makers could take timely and effective measures to deal with, making the negative impact of emergencies can be reduced as much as possible to maintain social stability.The simulation results show that the emergency event detection method has a better event detection effect in the real-time blog post data stream.Compared with the existing methods, the proposed method can meet the needs of emergency detection.Not only the detailed information of the sub-events can be detected, but also the related information of the events can be accurately detected.

Key words: emergency, word relationship graph, multi-attribute spectral clustering, detection

CLC Number:

TP393

Weijin JIANG, Yang WANG, Xiaoliang LIU, Sijian LYU. Multi-attribute spectral clustering emergency detection based on word correlation feature[J]. Journal on Communications, 2020, 41(12): 193-204.

Figures/Tables 14

References 29

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序号	字段名称	字段类型	描述
1	user_id	long	用户ID
2	user_name	string	用户昵称
3	content	string	微博内容
4	zan	Int	点赞数
5	zhuan	int	转发数
6	time	timestamp	发布时间

阈值	Precision	Recall	F1
0.5	88.92%	77.23%	81.94%
0.6	84.98%	82.24%	84.02%
0.7	82.57%	87.95%	85.11%
0.8	75.1%	90.07%	82.01%
0.9	63.33%	90.12%	73.99%

编号	子图单词数量	单词重合率
1	9	1.0
2	9	0.95
3	10	0.9
4	11	0.85
5	10	0.9
6	9	0.8
7	8	0.85

Multi-attribute spectral clustering emergency detection based on word correlation feature

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