Self-adaptive Method Based on Double-vector Model for Microblog Topic Tracking
HUANG Chang,GUO Wen-zhong,GUO Kun
(College of Mathematics and Computer Sciences,Fuzhou University,Fuzhou 350116,China)(Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China)(Key Laboratory of Spatial Data Mining & Information Sharing,Ministry of Education,Fuzhou 350116,China)
Abstract：In order to handle the characteristics of microblog such as short texts,continuous emergence of network neologisms and topic drifting,an adaptive microblog topic tracking method based on Double-Vector model is proposed.Firstly,a Double-Vector model is proposed to transform texts into vectors with word embedding technology and VSM(Vector Space Model),so that the text semantics is preserved and the problem of microblog neologisms is solved.Secondly,the similarity between a microblog and a topic is represented by the cosine value of the Double-Vector model of the microblog and the Double-Vector model of the topic.Thirdly,the similarity between a microblog and a topic is compares with the similarity threshold that is obtained by self-adaptive learning to determine whether the microblog is topic relevant microblog or not.Finally,through self-adaptive updating the topic model,the topic drift aroused by the development of microblog topics can be effectively overcomed.Experimental results show that the proposed method can effectively track the changes of the topic in real time and reduce the missing rate and false positive rate of the topic related microblog.
黄畅,郭文忠,郭昆. 基于双向量模型的自适应微博话题追踪方法[J]. 小型微型计算机系统, 2019, 40(6): 1203-1209.
HUANG Chang,GUO Wen-zhong,GUO Kun. Self-adaptive Method Based on Double-vector Model for Microblog Topic Tracking. Journal of Chinese Computer Systems, 2019, 40(6): 1203-1209.