期刊文献+

基于关联规则的党的十九大报告关键词相关性分析 被引量:5

A Correlation Analysis of Key Words in Report of the 19th National Congress of the Communist Party of China Based on Association Rules
在线阅读 下载PDF
导出
摘要 党的十九大报告提出了习近平新时代中国特色社会主义思想,如何深入学习和分析十九大报告成为当下的一大研究热点。本文运用word2vec和TFIDF对党的十九大报告的内容进行分析,对其中的关键词进行相似度计算,利用Python与Weka实现报告中关键词的关联规则挖掘。通过挖掘计算发现,报告中"中华民族""发展""文明""小康社会"等词语与样本中提取的50个关键词联系较为紧密。在规则中,许多关键词均与小康社会建设有较强的关联度,且部分关键词之间具有特定的相关关系。 The report of the 19 th National Congress of the Communist Party of China puts forward the socialistic thoughts with Chinese characteristics in the new era,how to deeply study and analyze the contents of the Report become research hotspot.word2 Vec and TFIDF are used in the paper to analyze the words in the Report and conduct similarity calculation on key words,Python and Weka are used to mine association rule of keywords in the report. According to data mining and calculation,there is a specific correlation among key words in the report. In the rules,many keywords have strong correlation with well-off society,and some key words have specific correlation. Based on the results of the above research,the paper provides a technical support of text quantization for analyzing the 19 th National Congress of the Communist Party of China.
作者 马琳琳 刘继 MA Linlin;LIU Ji(Xinjiang University of Finance and Economics,Urumqi 830012,China)
机构地区 新疆财经大学
出处 《新疆财经大学学报》 2018年第2期20-28,共9页 Journal of Xinjiang University of Finance & Economics
基金 国家自然科学基金项目"基于网络社群的网络舆情演化分析及突发事件预警机制研究"(项目编号:71261025) 新疆维吾尔自治区普通高等学校教学改革研究项目"大数据背景下实践教学知识网络创新体系研究"(项目编号:2017JG016)
关键词 数据挖掘 关联规则 文本分析 党的十九大报告 data mining association rule text analysis the report of the 19th National Congress of the Communist Party of China
作者简介 马琳琳(1994-),女,新疆财经大学统计与信息学院硕士研究生,研究方向为网络舆情、数据挖掘;;刘继(1974-),男,管理科学与工程博士,新疆财经大学统计与信息学院教授,研究方向为网络舆情、数据挖掘。
  • 相关文献

参考文献9

二级参考文献70

  • 1邹娟,周经野,邓成.一种基于语义分析的中文特征值提取方法[J].计算机工程与应用,2005,41(36):164-166. 被引量:6
  • 2谈文蓉,符红光,刘莉,杨宪泽.一种基于贝叶斯分类与机读词典的多义词排歧方法[J].计算机应用,2006,26(6):1389-1391. 被引量:5
  • 3何玉,冯剑琳,王元珍.基于最大关联规则的文本分类[J].计算机科学,2006,33(11):143-145. 被引量:6
  • 4Rennie J D M,Shih L,Teevan J,et al.Tackling the poor assumptions of Naive Bayes text classifiers [C]//Proceedings of the Twentieth International Conference on Machine Learning,2003,2:616-623.
  • 5Chiang J H,Chen Y C.Hierarchical fuzzy-KNN networks for news documents categorization[C]//lOth IEEE International Conference on Fuzzy Systems,2001(2) :720-723.
  • 6Sebastiani F,Nazionale C,Valdambrini N.An improved boosting algorithm and its application to text categorization[C]//Proceedings of the Ninth International Conference on Information and Knowledge Management, 2000: 78-85.
  • 7Zhang Hao,Berg A C,Maire M,et al.SVM-KNN:Discriminative nearest neighbor classification for visual category recognition[C]// IEEE Computer Society Conference on HHComputer Vision and Pattern Recognition, 2006 : 2126-2136.
  • 8Yang Y.An evaluaton of statistical approaches to text categorization[J].Information Retrieval, 1999,1 ( 1 ) : 76-78.
  • 9Komarek P,Moore A.Fast robust logistic regression for large sparse datasets with binary outputs[C]//Proceedings of the Ninth International Workshop on Artifical Intelligence and Statistics,2003:197-204.
  • 10Keerth S S,Duan K B,Shevade S K,et al.A fast dual algorithm for kernel logistic regression[J].Machine Learning,2005,61( 1 ) : 151-165.

共引文献246

同被引文献39

引证文献5

二级引证文献31

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