主观性句子情感倾向性分析方法的研究被引量：10

Research on Sentiment Orientation Analysis for Subjective Sentences

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摘要随着电子商务的飞速发展,用户评论信息对潜在顾客、商家和商品生产商的影响越来越大。由于在线的评论信息十分海量,所以很难通过人工浏览方式进行全面获取。评论句子往往具有很强的主观性,本文提出了整体方案帮助解决评论信息的获取、处理和可视化显示。通过利用词语的相似性计算方法和字的情感倾向分布概率计算方法,实现了极性词典的倾向值量化计算和极性词典的自动扩展。通过利用语义角色标注实现对评论句子的浅层语义分析,并利用统计结果设计出计算句子细粒度情感倾向值的方法。实验结果证明,基于语义角色标注方法比基于词性标注方法在句子细粒度情感倾向值计算中更有效。 With the rapid development of e-commerce,the influence of user comments on potential customers, merchants and manufacturers has become greater.There are a large number of new reviews emerged on Internet everyday, so it is difficult to acquire and process all information artificially.Reviews always have strong subjectivity,this paper proposes a whole scenario that helps to solute the acquirement,processing and visualization of reviews information.Through researching on computational methods of semantic similarity between words and sentiment orientation of character,this paper implements sentiment orientation quantization of polarity words and automatic expanding of lexicon.We also propose a method of making simple semantic analysis for sentences using semantic role labeling tool,and design an approach of calculating fine-grained sentiment orientation.The result shows that these methods are more available to analysis of sentiment orientation than the method based on POS.

作者施寒潇厉小军

机构地区浙江工商大学计算机与信息工程学院

出处《情报学报》 CSSCI 北大核心 2011年第5期522-529,共8页 Journal of the China Society for Scientific and Technical Information

基金浙江省自然科学基金资助项目（Y1090688）浙江省重大科技专项（2008C13082）

关键词情感倾向分析意见挖掘语义角色标注 sentiment orientation analysis opinion mining semantic role labeling

分类号 H146.3 [语言文字—汉语]

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1Turney P D. Thumbs up or thumbs down? Sentiment orientation applied to unsupervised classification of reviews[ C ]// Proc. of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002). Philadelphia: ACL, 2002: 417-424.
2Tsou B K Y, Yuen R W M, KwongO Y, et al. Polarity Classification of Celebrity Coverage in the Chinese Press [ C ]//Proceeding of the 2005 International Conference on Intelligence Analysis. McLean, Virginia, 2005. Published on web, http://analysis, mitre, org/ proceedings/index, html.
3Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment Classification Using Machine Learning Techniques[ C]// Proc. of the 2004 Conf. on Empirical Methods in Natural Language Processing ( EMNLP 2002 ). Philadelphia: ACL, 2002 : 79-86.
4Pang B, Lee L. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales[ C]// Proc. of the 43th Annual Meeting of the Association for Computational Linguistics (ACL 2005). Ann Arbor: ACL, 2005 : 115 -124.
5Mullen T, Collier N. Sentiment analysis using support vector machines with diverse information sources [ C ]// Proc. of the 2004 Conf. on Empirical Methods in Natural Language Processing EMNLP 2004 . Barcelona: ACL, 2004 : 412-418.
6Whitelaw C, Garg N, Argamon S. Using appraisal groups for sentiment analysis [ C ]// The ldth ACM Conf. on Information and Knowledge Management (CIKM 2005 ). Bremen : ACM Press, 2005 : 625-631.
7Hu M, Liu B. Mining and summarizing customer reviews [ C ]//Proc. of the 2004 ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining (KDD 2004). Washington: ACM Press, 2004: 168-177.
8Liu B, Hu M, Cheng J S. Opinion Observer: Analyzing and Comparing Opinions on the Web[ C]// Prof. of the 14th International World Wide Web Conference (WWW 2005). Chiba: ACM Press, 2005: 342-351.
9Miao Q, Li Q, Dai R. AMAZING: A Sentiment Mining and Retrieval System [ J ]. Expert Systems with Applications, 2009, 36(3): 7192-7198.
10Ku L W, Huang T H, Chen H H. Using Morphological and Syntactic Structures for Chinese Opinion Analysis [ C]// Proc. of the 2009 Conf. on Empirical Methods in Natural Language Processing (EMNLP 2009 ). Singapore: ACL, 2009 : 1260-1269.