A prediction model of micro-blog affective hotspots based on SVM collaborative filtering recommendation model

作     者：Dianhui, Mao Zihao, Song 

作者机构：School of Computer and Information Engineering Beijing Technology and Business University Beijing China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China 

出 版 物：《International Journal of Computers and Applications》 (Int J Comput Appl)

年 卷 期：2021年第43卷第2期

页      面：176-180页

核心收录：

学科分类：0303[法学-社会学] 1205[管理学-图书情报与档案管理] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors are funded by the research projects of The Social Science and Humanity on Young Fund of the ministry of Education [17YJCZH127] and the Fund of the social science and Nature Science of Beijing Technology and Business University [LKJJ2017-13] 

主　　题：Collaborative filtering 

摘      要：As innumerable events are reported on Weibo every day, it becomes especially important to predicate the event development trends early as possible. Weibo has been indispensable to the public life;its topic heat predication has become one of the hotspot subjects in data excavation for it provides the basis for public opinion monitoring. In this paper, a non-parametric method is adopted to predicate the heat variation of topic. This method, while maintaining the lower error rate, can effectively predicate whether the topic is heating up. Through the experiment, the parameters can be set flexibly to balance the detection time, true positive rate and false positive rate. The algorithm proposed in this research is effective and extendible, it is believed to help the Sina weibo developer and government in public opinion monitoring. According to this requirement, a non-parametric method is proposed to predicate the development trend of Sina weibo topics. © 2018 Informa UK Limited, trading as Taylor & Francis Group.