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New Technology of Library and Information Service  2015, Vol. 31 Issue (2): 55-63    DOI: 10.11925/infotech.1003-3513.2015.02.08
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Credibility Research on Chinese Online Customer Reviews
Hao Mei1, Yang Xiaoyuan2
1. Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China;
2. School of Management, Fudan University, Shanghai 200433, China
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Abstract  

[Objective] This paper proposes a review credibility sorting model in order to assist customers to make the best shopping decision. [Methods] The review credibility indexes are adjusted and optimized on the Visual Studio application development platform. Through questionnaire investigation to obtain the indexes score, credibility sorting model is constructed by Fuzzy Analytic Hierarchy Process. [Results] The experiment resualts show that compared with the Web original reviews, the new reviews sorting method is more scientific and reasonable. Those reviews without “helpful vote” are not necessarily unreliable, so the “helpful vote” is important to review credibility, but not the only factor which determines the credibility. [Limitations] People have different attitudes on factor's weight, so the future work should attach more importance to the expertise of rating factors. [Conclusions] The sorting model in this paper synthesizes several indexes and adjustment methods, thus it provides a new credibility sorting method which considering objective information and semantic features for the Chinese online customer reviews.

Key wordsCustomer review      Credibility      Text mining      FAHP      Sorting     
Received: 06 August 2014      Published: 17 March 2015
:  TP391  

Cite this article:

Hao Mei, Yang Xiaoyuan. Credibility Research on Chinese Online Customer Reviews. New Technology of Library and Information Service, 2015, 31(2): 55-63.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.02.08     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I2/55

[1] Noort G V, Kerkhof P, Fennis B M. The Persuasiveness of Online Safety Cues: The Impact of Prevention Focus Compatibility of Web Content on Consumers' Risk Perceptions, Attitudes, and Intentions [J]. Journal of Interactive Marketing, 2008, 22(4): 58-72.
[2] Racherla P, Friske W. Perceived ‘Usefulness' of Online Consumer Reviews: An Exploratory Investigation Across Three Services Categories [J]. Electronic Commerce Research and Applications, 2012, 11(6): 548-559.
[3] Utz S, Kerkhof P, van den Bos J. Consumers Rule: How Consumer Reviews Influence Perceived Trustworthiness of Online Stores [J]. Electronic Commerce Research and Applications, 2012, 11(1): 49-58.
[4] 毕继东, 胡正明. 网络口碑传播研究综述[J]. 情报杂志, 2010, 29(1): 11-16. (Bi Jidong, Hu Zhengming. A Review on the Research of Internet Word of Mouth Communication [J]. Journal of Information, 2010, 29(1): 11-16.)
[5] Liu Y, Huang X, An A, et al. Modeling and Predicting the Helpfulness of Online Reviews [C]. In: Proceedings of the 8th IEEE International Conference on Web Mining. IEEE, 2008: 443-452.
[6] Mudambi S M, Schuff D. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com [J]. MIS Quarterly, 2010, 34(1): 185-200.
[7] 刘逶迤, 逯万辉, 丁晟春. 商品评论信息可信度研究[J]. 情报科学, 2010, 30(10): 1556-1559. (Liu Weiyi, Lu Wanhui, Ding Shengchun. Research on the Credibility of Commodity Reviews Information [J]. Information Science, 2010, 30(10): 1556-1559.)
[8] 孟美任, 丁晟春. 在线中文商品评论可信度研究[J]. 现代 图书情报技术, 2013(9): 60-66. (Meng Meiren, Ding Shengchun. Research on the Credibility of Online Chinese Product Reviews [J]. New Technology of Library and Information Service, 2013(9): 60-66.)
[9] 龚思兰, 丁晟春, 周夏伟, 等. 在线商品评论信息可信度 影响因素实证研究[J]. 情报杂志, 2013, 32(11): 202-206. (Gong Silan, Ding Shengchun, Zhou Xiawei. An Empirical Research of Online Commodity Reviews Information Credibility Factors [J]. Journal of Intelligence, 2013, 32(11): 202-206.)
[10] 李志宇. 在线商品评论效用排序模型研究[J]. 现代图书情 报技术, 2013(4): 62-68. (Li Zhiyu. Study on the Reviews Effectiveness Sequencing Model of Online Products [J]. New Technology of Library and Information Service, 2013(4): 62-68.)
[11] Popescu A M, Etzioni O. Extracting Product Features and Opinions from Reviews [C]. In: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada. 2005: 339-346.
[12] Hu M, Liu B. Mining and Summarizing Customer Reviews [C]. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2004: 168-177.
[13] 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. (Li Shi, Ye Qiang, Li Yijun, et al. Mining Features of Products from Chinese Customer Online Reviews [J]. Journal of Management Sciences in China, 2009, 12(2): 142-152.)
[14] 张吉军. 模糊层次分析法 (FAHP) [J]. 模糊系统与数学, 2000, 14(2): 80-88. (Zhang Jijun. Fuzzy Analytical Hierarchy Process [J]. Fuzzy Systems and Mathematics, 2000, 14(2): 80-88.)
[15] 张吉军. 模糊一致判断矩阵 3 种排序方法的比较研究[J]. 系统工程与电子技术, 2003, 25(11): 1370-1372. (Zhang Jijun. Comparison of Three Ranking Methods for the Fuzzy Consistent Judgement Matrix [J]. Systems Engineering and Electronics, 2003, 25(11): 1370-1372.)
[16] Zhang K, Cheng Y, Liao W, et al. Mining Millions of Reviews: A Technique to Rank Products Based on Importance of Reviews [C]. In: Proceedings of the 13th International Conference on Electronic Commerce. New York, USA: ACM, 2011: 1-8.
[17] O'Mahony M P, Smyth B. Using Readability Tests to Predict Helpful Product Reviews [C]. In: Proceedings of the 9th International Conference on Adaptivity, Personalization and Fusion of Heterogeneous Information, Paris, France. 2010: 164-167.
[18] GooSeeker. MetaSeeker [EB/OL]. [2014-05-09]. http://www.gooseeker.com/cn/node/product/front.
[19] Sturges H A. The Choice of a Class Interval [J]. Journal of the American Statistical Association, 1926, 21(153): 65-66.
[20] Zhang Z. Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications [J]. IEEE Intelligent Systems, 2008, 23(5): 42-49.
[21] NLPIR 汉语分词系统[EB/OL].[2014-05-01]. http://ictclas.nlpir.org/.(NLPIR [EB/OL]. [2014-05-01]. http://ictclas.nlpir.org/.)
[22] CNNIC. 第33 次中国互联网发展状况统计报告[R]. 2014: 22-23. (CNNIC. Statistical Report on Internet Development in China [R]. 2014:22-23.)

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