thedistributedrepresentationforsocietalriskclassificationtowardbbsposts

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	thedistributedrepresentationforsocietalriskclassificationtowardbbsposts
	Chen Jindong; Tang Xijin
	2017
Source Publication	journalofsystemssciencecomplexity
ISSN	1009-6124
Volume	30 Issue:3 Pages:627
Abstract	The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.
Language	英语
Funding Project	[National Natural Science Foundation of China]
Document Type	期刊论文
Identifier	http://ir.amss.ac.cn/handle/2S8OKBNM/45865
Collection	系统科学研究所
Affiliation	中国科学院数学与系统科学研究院
Recommended Citation GB/T 7714	Chen Jindong,Tang Xijin. thedistributedrepresentationforsocietalriskclassificationtowardbbsposts[J]. journalofsystemssciencecomplexity,2017,30(3):627.
APA	Chen Jindong,&Tang Xijin.(2017).thedistributedrepresentationforsocietalriskclassificationtowardbbsposts.journalofsystemssciencecomplexity,30(3),627.
MLA	Chen Jindong,et al."thedistributedrepresentationforsocietalriskclassificationtowardbbsposts".journalofsystemssciencecomplexity 30.3(2017):627.

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