[目的/意义] 鉴于网络用户评价已成为人们选择产品或服务时的重要参考指标,旨在了解打分评价和评论评价之间的关系,同时为仅有评论评价的网站提供符合潜在分值的排序和推荐功能。[方法/过程] 通过抓取豆瓣电影的用户评价,使用ROST EA工具进行情感分析得到评论评价的综合情绪值,将其与打分评价进行相关分析,并考虑评论文本的情感强度赋权对结果造成的差异,在此基础上通过回归分析构建回归模型并对其进行检验。[结果/结论] 发现评论评价的综合情绪值与打分评价相关性较高,情感强度的赋权情况对结果影响不大,说明可以根据评论评价预测打分,据此给出相应的回归模型。
[Purpose/significance] Internet user evaluation has become an important reference index when people choose goods. This paper aims to know the relationship between grade evaluation and review evaluation, to provide ranking and recommendation function in line with the potential grade for these websites which only have review evaluation.[Method/process] Through grasping Douban.com Movie users' comments and using the ROST EA tool for sentiment analysis to get comprehensive sentiment value, then makes correlation analysis with grade evaluation and taking into account the emotional intensity of the comment text, which cause difference of the results. On the basis of this, a regression model is constructed by regression analysis and tested.[Result/conclusion] It turns out that the correlation between them was high and the empowerment of emotional strength has little effect on the results, which indicates we can predict grade by review evaluation. The corresponding regression model is given in this paper.
[1] GODES D, MAYZLIN D. Using online conversations to study word-of-mouth communication[J]. Marketing Science, 2004,23(4):545-560.
[2] 中国互联网络信息中心.2013年中国网络购物市场研究报告[DB/OL].[2015-09-12].http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/dzswbg/201404/P020140421360912597676.pdf,2014.
[3] 郝媛媛,邹鹏,李一军,等.基于电影面板数据的在线评论情感倾向对销售收入影响的实证研究[J].管理评论, 2009,21(10):95-103.
[4] 姚天昉,程希文,徐飞玉,等.文本意见挖掘综述[J]. 中文信息学报, 2008,22(3):71-80.
[5] 赵妍妍,秦兵,刘挺.文本情感分析[J].软件学报, 2010,21(8):1834-1848.
[6] 张紫琼,叶强,李一军.互联网商品评论情感分析研究综述[J].管理科学学报, 2010,13(6):84-96.
[7] KATZ E, LAZARSFELD P F. Personal influence:the part played by people in the flow of mass communications[M]. New York:Free Press, 1955.
[8] GHOSE A, IPEIROTIS P G. Designing novel review ranking systems:predicting the usefulness and impact of reviews[C]//Proceedings of the ninth international conference on electronic commerce. New York:ACM, 2007:303-310.
[9] MUDAMBI S M, SCHUFF D. What makes a helpful review? A study of customer reviews on Amazon. com[J]. MIS quarterly, 2010, 34(1):185-200.
[10] 殷国鹏, 刘雯雯, 祝珊.网络社区在线评论有用性影响模型研究——基于信息采纳与社会网络视角[J].图书情报工作, 2012, 56(16):140-147.
[11] 陈江涛, 张金隆, 张亚军.在线商品评论有用性影响因素研究:基于文本语义视角[J].图书情报工作, 2012, 56(10):119-123.
[12] HATZIVASSILOGLOU V, McKEOWN K R. Predicting the semantic orientation of adjectives[C]//Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and eighth conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 1997:174-181.
[13] PANG 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 Language Processing-volume 10. Stroudsburg:Association for Computational Linguistics, 2002:79-86.
[14] TURNEY P D. Thumbs up or thumbs down?:semantic orientation applied to unsupervised classification of reviews[C]//Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2002:417-424.
[15] TURNEY P D, MICHAEL L. Measuring praise and criticism:inference of semantic orientation From association[J]. ACM transactions on information systems, 2003, 21(4):315-346.
[16] WIEBE J M. Learning subjective adjectives from corpora[A]//Proceedings of the 17th National Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2000:735-740.
[17] 叶强,张紫琼,罗振雄.面向互联网评论情感分析的中文主观性自动判别方法研究[J].信息系统学报, 2007,1(1):79-91.
[18] HU M, LIU B. Mining opinion features in customer reviews[C]//Proceedings of the 19th National Conference on Artifical Intelligence.Menlo Park:AAAI Press, 2004:755-760.
[19] 李实,叶强,李一军,等.中文网络客户评论的产品特征挖掘方法研究[J].管理科学学报, 2009,12(2):142-152.
[20] 田韶存. 在线社区用户评论有用性研究[D]. 济南:山东大学, 2014.
[21] 杨雅秀. 在线评论对创新扩散影响的实证研究[D].杭州:浙江大学, 2012.
[22] 闫强,孟跃.在线评论的感知有用性影响因素——基于在线影评的实证研究[J].中国管理科学, 2013,21(S1):126-131.
[23] 殷国鹏.消费者认为怎样的在线评论更有用?——社会性因素的影响效应[J].管理世界,2012(12):115-124.
[24] 刘志明.跨文化视角下在线评论有用性研究——基于说服双过程模型[J].江汉学术, 2015,34(4):76-85.
[25] BISMIL R, DUDEK N L, WOOD T J. In-training evaluations:developing an automated screening tool to measure report quality[J]. Medical education, 2014, 48(7):724-732.
[36] BASIRI M E, GHASEM-AGHAEE N, NAGHSH-NILCHI A R. Exploiting reviewers' comment histories for sentiment analysis[J]. Journal of information science, 2014, 40(3):313-328.
[27] BAUER N S, CARROLL A E, DOWNS S M. Understanding the acceptability of a computer decision support system in pediatric primary care[J]. Journal of the American Medical Informatics Association, 2014, 21(1):146-153.
[28] KU L W, WU T H, LEE L Y, et al. Construction of an evaluation corpus for opinion extraction[C]//Proceedings of NTCIR-5 workshop meeting.Tokyo:NII, 2005:513-520.
