期刊文献+

基于超短评论的图书领域情感词典构建研究 被引量:14

Research on the Construction of Sentiment Lexicon in Book Field Based on Extreme Short Reviews
下载PDF
导出
摘要 [目的/意义]图书作为典型的体验型产品,缺乏与之对应的领域情感词典,限制了该领域中的用户情感分析与情感挖掘研究,文章提出一种基于超短评论的图书领域情感词典构建方法。[方法/过程]在定义超短评论概念的基础上,利用规则组合进行候选词过滤,提出一种改进点互信息的方法进行情感词极性判断,并利用用户投票方法确定词汇的情感强度。[结果/结论]在图书评论场景中,本文情感词典的准确率、召回率、词典规模均远高于通用情感词典HowNet、大连理工情感本体,新词发现能力和实际应用能力较强。 [Purpose/significance]As a typical experiential product,books lack the corresponding domain emotion dictionary,which limits the research on user sentiment analysis and sentiment mining in this field.This paper proposes a method of constructing Book domain sentiment dictionary based on extreme short comments.[Method/process]On the basis of defining the concept of ultrashort comment,the candidate words are filtered by rule combination,and an improved mutual information method is proposed to judge the polarity of emotional words,and the user voting method is used to determine the emotional intensity of words.[Result/conclusion]In the scene of book review,the accuracy rate,recall rate and dictionary scale of this emotional dictionary are much higher than those of HowNet and Dalian Institute of technology,and the ability of new word discovery and practical application is strong.
作者 周知 王春迎 朱佳丽 Zhou Zhi
出处 《情报理论与实践》 CSSCI 北大核心 2021年第9期183-189,197,共8页 Information Studies:Theory & Application
基金 国家社会科学基金青年项目“网络知识社区用户交互内容的组织与传播研究”的成果,项目编号:18CTQ033。
关键词 情感词典 情感分析 超短评论 点互信息 图书领域 sentiment lexicon sentiment analysis extremeshort review pointwise mutual information library field
  • 相关文献

参考文献4

二级参考文献64

  • 1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
  • 2Jansen B J, Zhang M, Sobel K, Chowdury A. Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 2009, 60(11): 2169-2188.
  • 3Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational Science, 2011, 2(1): 1-8.
  • 4Zhao J, Dong L, Wu J, Xu K. MoodLens: An emoticon- based sentiment analysis system for Chinese tweets. In Proc. the 18th KDD, Aug. 2012, pp.1528-1531.
  • 5Jiang L, Yu M, Zhou M, Liu X, Zhao T. Target-dependent Twitter sentiment classification. In Proc. the 49th ACL, Jun. 2011, pp.151-160.
  • 6Liu K L, Li W J, Guo M. Emoticon smoothed language models for Twitter sentiment analysis. In Proe. the 26th AAAI. Jul. 2012.
  • 7Bermingham A, Smeaton A F. Classifying sentiment in mi- croblogs: Is brevity an advantage? In Proc. the 19th ACM International Conference on Information and Knowledge Management, Oct. 2010, pp.1833-1836.
  • 8Kouloumpis E, Wilson T, Moore J. Twitter sentiment anal- ysis: The good the bad and the OMG! In Proc. the 5th ICWSM, Jul. 2011.
  • 9Barbosa L, Feng J. Robust sentiment detection on Twitter from biased and noisy data. In Proc. the 23rd International Conference on Computational Linguistics: Posters, Aug. 2010, pp.36-44.
  • 10Pak A, Paroubek P. Twitter as a corpus for sentiment anal- ysis and opinion mining. In Proe. LREC, May 2010.

共引文献77

同被引文献235

引证文献14

二级引证文献77

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