文献类型：期刊文献

英文题名：Model of Network Topic Detection Based on Web Usage Behaviour Mode Analysis and Mining Technology

作者：Chen, M.[1,2]

第一作者：陈默;Chen, M.

通讯作者：Chen, M[1];Chen, M[2]

机构：[1]Beijing Union Univ, Coll Business, A3 Yanjingdongli, Beijing 100025, Peoples R China;[2]Renmin Univ China, Sch Informat, 59 Zhongguancun St, Beijing 100872, Peoples R China

第一机构：北京联合大学商务学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Business, A3 Yanjingdongli, Beijing 100025, Peoples R China;[2]corresponding author), Renmin Univ China, Sch Informat, 59 Zhongguancun St, Beijing 100872, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

年份：2017

卷号：12

期号：2

起止页码：183-200

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL

收录：;Scopus(收录号：2-s2.0-85014795228);WOS:【SCI-EXPANDED(收录号:WOS:000395506500003)】；

基金：This work was supported by the National Natural Science Foundation of China under Grant Nos. 71572015, the National Natural Science Foundation of China under Grant Nos. 71271209, the Project of Philosophy and Social Sciences Planning in Beijing under Grant Nos. 13JGC090.

语种：英文

外文关键词：web usage behaviour; network topic detection; clicking mode analysis; retrieval mode analysis

摘要：This research has caught researchers' wide attention for detecting network topic exactly with the arrival of big data era characterized by semi-structured or unstructured text. This paper proposes a model of network topic detection based on web usage behaviour mode analysis and mining technology taking Web news as object of research. The author elaborates main function and method proposed in this model, which include the analysis module of Web news instance clicking mode, the analysis module of Web news instance retrieval mode, the analysis module of Web news instance seed and the analysis module of similar Web news instance supporting topics. Based on these functions and methods, the author elaborates main algorithm proposed in this model, which include the mining algorithm of Web news seed instances and the mining algorithm of similar Web news instances supporting topics. These functional algorithms have been applied in processing module of model, and focus on how to detect network topic efficiently from a large number of web usage behaviour towards to Web news instances, in order to explore a research method for network topic detection. The process of experimental analysis includes three steps, firstly, the author analyses the precision of topic detection under different method, secondly, the author completes the impact analysis of Web news topic detection quality from the number of Web news instances concerned and seed threshold, finally, the author completes the quality impact analysis of Web news instances mined supporting topic from the number of Web news instances concerned and probability threshold. The results of experimental analysis show the feasibility, validity and superiority of model design and play an important role in constructing topic-focused Web news corpus so as to provide a real-time data source for topic evolution tracking.