Mining micro-blogging users interest features via fingerprint generation

摘要：

　　Nowadays,micro-blogging is widely used as a communication and information sharing social network service,therefore mining micro-blogging users behavior features is very important both in the economic and social fields.A framework for the analysis of users interest features is proposed in this paper.After data cleaning,word segmentation,POS(part of speech) filtering and synonym merging,the keywords that called terms of all the tweets posted by a typical user in 2011 are extracted.Then VSM(vector space model) is used to generate the feature vector of the tweets from these terms.Furthermore,a k-blt binary called fingerprint is generated from the high dimensional feature vector of the tweets by use of Simhash algorithm.The micro-blogging users interest features and change patterns could be detected by analyzing the fingerprint sequences and the distance between the adjacent two fingerprints.Taking Sina micro-blogging as background,a series of experiments are done to prove the effectiveness of the algorithms.

关键词： micro-blogging interest feature tweet fingerprint

作者: Dong Liu Quanyuan Wu Weihong Han

作者单位: School of Computer National University of Defense Technology Changsha,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1470-1473

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）