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New Technology of Library and Information Service  2013, Vol. Issue (4): 62-68    DOI: 10.11925/infotech.1003-3513.2013.04.10
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Study on the Reviews Effectiveness Sequencing Model of Online Products
Li Zhiyu
School of Information Management, Central China Normal University, Wuhan 430079, China
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Abstract  On the basis of studying the influencing factors of online reviews effectiveness, a review effectiveness index system is established. The fuzzy analytic hierarchy process is adopted to determine the relative weight of indexes, various indexes of reviews content are quantized by semantic mining, and the total effectiveness score is calculated for each review. In terms of the model application of this study, nearly 2 000 reviews on a product of China’s Tmall are selected to make an empirical analysis. The study and comparison indicates that, after being processed by the sequencing model, a large number of useless reviews are postponed, and those reviews at the forefront of the new sequence are very rich in information content and high in effectiveness, and can assist consumers in making purchase decisions effectively.
Key wordsInformation mining      Online reviews      Effectiveness sequencing     
Received: 19 March 2013      Published: 17 June 2013
:  F224  

Cite this article:

Li Zhiyu. Study on the Reviews Effectiveness Sequencing Model of Online Products. New Technology of Library and Information Service, 2013, (4): 62-68.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2013.04.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2013/V/I4/62

[1] Chevalier J A, Mayzlin D. The Effect of Word of Mouth on Sales: Online Book Reviews[J]. Journal of Marketing Research,2006,43(3):345-354.
[2] Ye Q,Zhang Z Q,Law R. Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches[J]. Expert Systems with Applications,2009, 36(3): 6527-6535.
[3] 张红斌,李广丽.商品在线评价的情感倾向性分析研究[J]. 现代图书情报技术, 2012(10):61-66.(Zhang Hongbin, Li Guangli. Research on Sentiment Orientation Analysis of Product Online Reviews[J]. New Technology of Library and Information Service, 2012(10):61-66.)
[4] 杨铭,祁巍,闫相斌,等. 在线商品评论的效用分析研究[J]. 管理科学学报,2012, 15(5):65-75.(Yang Ming,Qi Wei,Yan Xiangbin,et al.Utility Analysis for Online Product Review[J].Journal of Management Sciences in China, 2012,15(5):65-75.)
[5] Miao Q L, Li Q D, Dai R W.AMAZING: A Sentiment Mining and Retrieval System[J].Expert Systems with Applications, 2009, 36(3): 7192-7198.
[6] Liu J J,Cao Y B,Lin C Y,et al. Low-quality Product Review Detection in Opinion Summarization[C].In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.Prague: Association Computational Linguistics,2007:334-342.
[7] Zhang Z.Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications[J].IEEE Intelligent Systems, 2008, 23(5):42-49.
[8] 郝媛媛,叶强,李一军.基于影评数据的在线评论有用性影响因素研究[J]. 管理科学学报,2010, 13(8):78-96.(Hao Yuanyuan,Ye Qiang,Li Yijun.Research on Online Impact Factors of Customer Reviews Usefulness Based on Movie Reviews Data[J].Journal of Management Sciences in China, 2010, 13(8):78-96.)
[9] 彭岚,周启海,邱江涛.消费者在线评论有用性影响因素模型研究[J]. 计算机科学,2011, 38(8):205-207.(Peng Lan, Zhou Qihai, Qiu Jiangtao. Research on the Model of Helpfulness Factors of Online Customer Reviews[J].Computer Science, 2011,38(8):205-207.)
[10] Lau R Y K,Liao S S Y,Xu K Q.An Empirical Study of Online Consumer Review Spam: A Design Science Approach[C]. In: Proceedings of the 31st International Conference on Information Systems,St.Louis,USA. Accociation of Information Systems,2010.
[11] Sen S,Lerman D.Why Are You Telling Me This? An Examination into Negative Consumer Reviews on the Web[J].Journal of Interactive Marketing, 2007, 21(4):76-94.
[12] Clemons E K, Gao G D, Hitt L M. When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry[J].Journal of Management Information Systems, 2006, 23(2):149-171.
[13] 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.
[14] GooSeeker. MetaSeeker[EB/OL].[2013-03-03].http://www.gooseeker.com/cn/node/product/front.
[15] GooSeeker. Bucket[EB/OL].[2013-03-03].http://www.gooseeker.com/cn/node/document/terms/bucket.
[16] 数据堂. 台湾大学NTUSD-简体中文情感极性词典[EB/OL].[2013-03-05].http://www.datatang.com/data/11837.(Data Tang. National Taiwan University-The Polarity of Simplified Chinese Emotional Dictionary [EB/OL].[2013-03-05].http://www.datatang.com/data/11837.)
[17] Hu M Q, Liu B. Mining Opinion Features in Customer Reviews [C]. In: Proceedings of the 19th National Conference on Artifical Intelligence (AAAI ’2004). Menlo Park: AAAI Press,2004:755-760.
[18] 余传明.从用户评论中挖掘产品属性——基于SOM的实现[J]. 现代图书情报技术,2009(5):61-66. (Yu Chuanming. Mining Product Aspects from User Reviews—An SOM-based Approch[J]. New Technology of Library and Information Service,2009(5):61-66.)
[19] Popescu A M, Etzioni O. Extracting Product Features and Opinions from Reviews [C].In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing.Stroudsburg: Association for Computational Linguistics,2005: 339-346.
[20] 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.)
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