[Objective] We propose a model to identify useful online Chinese reviews, which helps consumers make purchasing decisions. [Methods] First, we calculated six attributes affecting the usefulness of online reviews based on their form and content characteristics. Then, we constructed a usefulness evaluation system with the weighted grey relational degree analysis method. Finally, we created a model to retrieve useful online reviews with k-means clustering method. [Results] We examined the effectiveness of our model with online reviews from Amazon.com. The recall, precision and F values showed that our method could effectively identify the useful online reviews, and classify the polarity ones. [Limitations] The samples, metrics and e-commerce platforms could be further improved. [Conclusions] The proposed method could rank and classify online reviews accurately and reliably.
Ngo-Ye T L, Sinha A P. The Influence of Reviewer Engagement Characteristics on Online Review Helpfulness: A Text Regression Model[J]. Decision Support Systems, 2014, 61(4): 47-58.
Huang A H, Chen K, Yen D C, et al.A Study of Factors that Contribute to Online Review Helpfulness[J]. Computers in Human Behavior, 2015, 48(C): 17-27.
Agnihotri A, Bhattacharya S.Online Review Helpfulness: Role of Qualitative Factors[J]. Psychology & Marketing, 2016, 33(11): 1006-1017.
Chua A Y K, Banerjee S. Helpfulness of User-generated Reviews as a Function of Review Sentiment, Product Type and Information Quality[J]. Computers in Human Behavior, 2016, 54(C): 547-554.
(Li Qigeng, Zhao Xiaohong, He Yaoyu.An Empirical Study of Influencing Factors of Perceived Usefulness of Online Review[J]. Information Studies: Theory & Application, 2017, 40(8): 122-125.)
(Zhang Yanhui, Li Zongwei, Zhao Yicheng.How the Information Quality Affects the Online Review Usefulness? ——An Empirical Analysis Based on Taobao Review Data[J]. Chinese Journal of Management, 2017, 14(1): 77-85.)
(Liu Jie, Fu Xiaodong, Liu Li, et al.Analysis on Popular B2C Shopping Site[J]. Computer Applications and Software, 2017, 34(3): 71-75, 97.)
Jindal N, Liu B.Opinion Spam and Analysis[C]//Proceedings of International Conference on Web Search and Web Data Mining, Califormia, USA. New York, NY, USA: ACM, 2008: 219-229.
Krishnamoorthy S.Linguistic Features for Review Helpfulness Prediction[M]. Pergamon Press, Inc., 2015.
Ngo-Ye T L, Sinha A P, Sen A. Predicting the Helpfulness of Online Reviews Using a Scripts-Enriched Text Regression Model[J]. Expert Systems with Applications, 2016, 71(11): 98-110.
(Guo Shunli, Zhang Xiangxian, Li Zhongmei.Study on the Usefulness Ranking Model of Mobile O2O Online Reviews from the Perspective of User’s Information Demand: Taking an Example of Meituan[J]. Library and Information Service, 2015, 59(12): 85-93.)
(Zhang Yanfeng, Li He, Zhai Qian, et al.Research on the Usefulness of Online Review Based on Fuzzy TOPSIS Analysis: A Case Study of Amazon’s Mobile Phone Review[J]. Library and Information Service, 2016, 60(13): 109-117,125.)
Zhang K, Cheng Y, Liao W, et al.Mining Millions of Reviews: A Technique to Rank Products Based on Importance of Reviews[C]//Proceedings of the 13th International Conference on Electronic Commerce. New York, USA: ACM, 2011: 1-8.
HowNet [EB/OL]. [2017-03-18]. .
数据堂. 台湾大学NTUSD-简体中文情感极性词典[EB/OL]. [2017-03-18]. .
(Data Tang. Taiwan University-The Polarity of Simplified Chinese Emotional Dictionary [EB/OL]. [2017-03-18].
(Dun Xinhui, Zhang Yunqiu, Yang Kaixi.Fine-grained Sentiment Analysis Based on Weibo[J]. Data Analysis and Knowledge Discovery, 2017, 1(7): 61-72.)
Li G, Hoi S C H, Chang K, et al. Microblogging Sentiment Detection by Collaborative Online Learning[C]//Proceedings of the 2010 IEEE International Conference on Data Mining, Sydney, Australia. USA: IEEE, 2010: 893-898.