期刊文献+

多时间序列关联规则分析的论坛舆情趋势预测 被引量:23

Forum Sentiment Trend Prediction Based on Multi Time Series Association Rule Analysis
下载PDF
导出
摘要 为了预测论坛舆情及其动态演变趋势,基于多时间序列的关联分析,集中分析了论坛中3个量的时间序列之间的关联规则:活跃者之间的关系强度的时间序列、坚定支持者人数的时间序列以及坚定支持者成员的变化频度的时间序列。然后给出了一种新的基于多时间序列关联分析的论坛舆情预测算法(Forum sentiment trend prediction based on multi time series association rule analysis,TPMTSA),并在真实数据集和拟合数据集上进行了大量的实验。结果表明:TPMTSA算法具有有效性和较高的运行效率。研究结果可用于论坛舆情预警监控。 In order to predict the e,,;olving trend of forum sentiment, based on the association analysis of multi time series, the association rules of three-quantity time series over forum sentiment are anlyzed, namely, the strength of relationship between actors, the number of pillars, and the changing frequency of pillars. Then a novel prediction algorithm, forum sentiment trend prediction based on multi time series association rule analysis(TPMTSA), is proposed. Extensive experiments over real and synthetic datasets are conducted. Results show the effectiveness and the efficiency of TPMTSA. The research re- suits can be used to monitor the forum opinion.
出处 《南京航空航天大学学报》 EI CAS CSCD 北大核心 2012年第6期904-910,共7页 Journal of Nanjing University of Aeronautics & Astronautics
基金 国家自然科学基金(61100060)资助项目 教育部人文社会科学研究青年基金(11YJCZH008)资助项目
关键词 论坛舆情 趋势预测 时间序列 关联分析 forum sentiment trend predicton time series association analysis
  • 相关文献

参考文献17

  • 1Shi Xiaolin, Zhu Jun, Cai Rui, et al. User grouping behavior in online forums[C]//International Confer- enee on Knowledge Discovery and Data Mining. Paris: ACM, 2009: 777-785. 被引量:1
  • 2Newman M E J. Finding community structure in networks using the eigenvectors of matrices [J]. Physical Reviem E, 2006,7,1 (3) : 036104. 被引量:1
  • 3Xu Kaiquan, Li Jiexun, Stephen S Y. Sentiment community detection In social networks [C]//Pro- ceedings of the 2011 iConJerence. Seattle: ACM, 2011:804-805. 被引量:1
  • 4Backstrom L, Huttenlocher D, Kleinberg J, et al. Group formation in large social networks: member- ship, growth, and evolution[C]//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphi- a: ACM, 2006: 44-54. 被引量:1
  • 5Aldashev G, Carlletti T. Benefits of diversity, com- munication costs, and public opinion dynamics[J].Complexity, 2009, 15(2): 54-63. 被引量:1
  • 6刘永建,朱剑英,夏洪山,郭亚中.飞机复杂系统故障诊断的灰色粗集推理方法[J].南京航空航天大学学报,2009,41(2):227-231. 被引量:3
  • 7刘大伟,陶来发,吕琛,刘红梅.飞机机电系统PHM的综合诊断推理机设计[J].南京航空航天大学学报,2011,43(B07):114-118. 被引量:5
  • 8Chi Yun, Song Xiaodan, Zhou Dengyong, et al. On evolutionary spectral clustering[J]. ACM Transac- tions on Knowlege Discovery from Data, 2009, 3 (4) : 1-30. 被引量:1
  • 9Newman M E J. Analysis of weighted networks[J]. Physieal Review E, 2004, 70(5): 056131. 被引量:1
  • 10Newman M E J, Girvan M. Finding and evaluating community structure in networks [J]. Physical Re- view E, 2004, 69(2): 026113. 被引量:1

二级参考文献40

共引文献6

同被引文献252

引证文献23

二级引证文献205

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部