The Distributed Representation for Societal Risk Classification toward BBS Posts  

作　　者：CHEN Jindong TANG Xijin 

机构地区：[1]Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China [2]China Academy of Aerospace Systems Science and Engineering,Beijing 100048,China

出　　处：《Journal of Systems Science & Complexity》2017年第3期627-644,共18页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.71171187,71371107,and 61473284

摘　　要：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.

关 键 词：Distributed representation KNN paragraph vector model societal risk classification Tianya forum. 

分 类 号：O211.67[理学—概率论与数理统计]