期刊文献+

情感词权值研究及在情感极性分析中的应用 被引量:5

Emotional term weight research and application to emotional polarity analysis
下载PDF
导出
摘要 目前,常用的情感词典为通用情感词典。对于这些情感词典会存在如下缺陷,如词的情感区分度不够,对于不同数据集适应性欠佳。针对以上问题,基于情感词的情感确定性,提出一种新的权值计算方法。即情感确定性越大,其权值也越大,词的情感倾向也更明确,反之亦然。这样,使得字典内的情感词形成不同的区分度,这也与现实相符合。基于情感词权值,使用相应评分方法,运用到文本情感极性分析中。通过实验结果表明,这种方法相比只使用基础情感词典进行情感极性判断,准确度提升大约8.9%。这也说明了这种权值计算方法是有效的,结果符合预期。 Currently,the common emotion dictionaries are used frequently. But,There is some defects in these emotion dictionaries,such as not enough discrimination of the term,and poor adaptability to data set. To solve these problems,this article proposed a new method to calculate the weight of emotional terms with emotion certainty. The greater the emotion certainty of the term is,the bigger the weight is,and the more explicit the sentiment is. Using emotional weight will make term in emotion dictionary form the different degree of differentiation,which is also consistent with reality. And based on the weight of the emotional term,the appropriate scoring method was used to the analysis of text sentiment polarity compared to the method only using basic emotion dictionary. The experimental results show that this method would upgrade about 8. 9% on accuracy for emotional polarity judgment,which illustrates this weight calculation method is effective,and consistent with expectation.
作者 阳林
出处 《计算机应用》 CSCD 北大核心 2015年第A02期125-127,共3页 journal of Computer Applications
关键词 情感词 情感确定性 权值 情感分析 评分 emotional term emotion certainty weight sentiment analysis scoring
  • 相关文献

参考文献12

  • 1魏韡,向阳,陈千.中文文本情感分析综述[J].计算机应用,2011,31(12):3321-3323. 被引量:70
  • 2朱嫣岚,闵锦,周雅倩,黄萱菁,吴立德.基于HowNet的词汇语义倾向计算[J].中文信息学报,2006,20(1):14-20. 被引量:326
  • 3赵鹏,赵志伟,卓景文.一种情感词语义加权的句子倾向性识别方法[J].计算机工程与应用,2011,47(35):161-163. 被引量:3
  • 4LIU B. Sentiment analysis and opinion mining[ J]. Synthesis Lec- tures on Human Language Technologies, 2012, 5(1) : 1 - 167.
  • 5TABOADA M, BROOKE J, TOFILOSKI M, et al. Lexicon-based methods for sentiment analysis [ J ]. Computational Linguistics, 2011, 37(2): 267-307.
  • 6PANG B, LEE L, VAITHYANATHAN S. Thumbs up?: sentiment classification using machine learning techniques [ C ]// Proceedings of the ACL-02 Conference on Empirical Methods in Natural Lan- guage Processing. Stroudsburg: Association for Computational Lin- guistics, 2002:79-86.
  • 7ABBASI A, CHEN H, SALEM A. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums [ J]. ACM Transactions on Information Systems, 2008, 26(3) : 12.
  • 8LIU J, SENEFF S. Review sentiment scoring via a parse-and-para- phrase paradigm[ C]// Proceedings of the 2009 Conference on Em- pirical Methods in Natural Language Processing. Stroudsburg: Asso- ciation for Computational Linguistics, 2009, 1 : 161 - 169.
  • 9JIA L, YU C, MENG W. The effect of negation on sentiment analy- sis and retrieval effectiveness[ C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York: ACM, 2009:1827-1830.
  • 10TITOV I, McDONALD R. Modeling online reviews with multi- grain topic models[ C ]// Proceedings of the 17th Intemational Conference on World Wide Web. New York: ACM, 2008:111 - 120.

二级参考文献59

  • 1杨频,李涛,赵奎.一种网络舆情的定量分析方法[J].计算机应用研究,2009,26(3):1066-1068. 被引量:19
  • 2朱嫣岚,闵锦,周雅倩,黄萱菁,吴立德.基于HowNet的词汇语义倾向计算[J].中文信息学报,2006,20(1):14-20. 被引量:326
  • 3孟凡博 蔡莲红 陈斌 等.文本褒贬倾向性判定系统的研究.小型微型汁算机系统,2008,28(1):14-20.
  • 4姚天昉,娄德成.汉语情感词语义倾向判别的研究[C]//中国计算技术与语言问题研究-第七届中文信息处理国际会议论文集,武汉:2007.
  • 5黄萱菁,赵军.中文文本情感倾向性分析[EB/OL】.http://www.dpr.ia.ac.cn/2008papers/gnhy/nh12.pdf.
  • 6王跟,赵军.中文词语倾向性的分析[EB/OL].(2010-04).http://www.nlpr.1abs.gov.cn/2006papers.
  • 7Riloff E, Wiebe J, Wilson T.Leaming subjective nouns using extraction pattern bootst-rapping[C]//Proceedings of Conf on Natural Language Learning(CoNLL),2003:25-32.
  • 8Hu Mingqing, Liu Bing.Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD,2004:168-177.
  • 9中文知网[EB/0L].[2010-05].http://www.keenage.com/html/c_index.html.
  • 10中文情感挖掘语料[EB/OL].(2010-05).http://www.searchforum.org.cn/tansongbo/corpus-senti.htm.

共引文献416

同被引文献88

引证文献5

二级引证文献33

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