Towards exploring when and what people reviewed for their online shopping experiences

Abstract

Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers. As a result, exploring reviewers’ opinions from a huge number of online reviews has become more important and simultaneously more difficult than ever before. In this paper, we first present a methodological framework to study the “purchasing-reviewing” behavior dynamics of online customers. Then, we propose a review-to-aspect mapping method to explore reviewers’ opinions from the massive and sparse online reviews. The analytical and experimental results with real data demonstrate that online customers can be sectioned into groups in accordance with their reviewing behaviors and that people within the same group may have similar reviewing motivations and concerns for an online shopping experience.

Keywords

E-commerce / online review / review dynamics / opinion mining

References

[1] Aggarwal C. C., Zhai C.. Aggarwal C. C., Zhai C.. A survey of text clustering algorithms. Mining Text Data, 2012 77-128.

[2] Almahairi A., Kastner K., Cho K., Courville A.. Learning distributed representations from reviews for collaborative filtering. the 9th ACM Conference on Recommender Systems, 2015 147-154.

[3] Brown B.. Beyond recommendations: local review web sites and their impact. ACM Transactions on Computer-Human Interaction, 2012, 19(4): 27:1-27:24.

[4] Chen G., Han X., Wang B.. Multi-level scaling properties of instant-message communications. Physics Procedia, 2010, 3(5): 1897-1905.

[5] Cheng, C.Y., Li, Eldon Y. & Shu, W. (2012). The effect of affinity design on review helpfulness: an experimental study of online passenger review in the airline industry. In the International Conference on Electronic Business (iceb), 147–153.

[6] Chintagunta P. K., Gopinath S., Venkataraman S.. The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 2010, 29(5): 944-957.

[7] Cui H., Mittal V., Datar M.. Comparative experiments on sentiment classification for online product reviews. In AAAI, 2006, 6: 1265-1270.

[8] Dave K., Lawrence S., Pennock D. M.. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In the 12th International Conference on World Wide Web, 2003 519-528.

[9] Gheorghiu S., Coppens M. O.. Heterogeneity explains features of “anomalous” thermodynamics and statistics. In the National Academy of Sciences of the United States of America, 2004, 101(45): 15852-15856.

[10] Ghose A., Ipeirotis P. G.. Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(10): 1498-1512.

[11] Gilbert E., Karahalios K.. Understanding deja reviewers. In the 2010 ACM Conference on Computer Supported Cooperative Work, 2010 225-228.

[12] Gomaa W. H., Fahmy A. A.. A survey of text similarity approaches. International Journal of Computer Applications, 2013, 68(13): 13-18.

[13] Hu M., Liu B.. Mining and summarizing customer reviews. In the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004 168-177.

[14] Hu N., Liu L., Zhang J. J.. Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 2008, 9(3): 201-214.

[15] Jo Y., Oh A. H.. Aspect and sentiment unification model for online review analysis. In the fourth ACM International Conference on Web Search and Data Mining, 2011 815-824.

[16] Johnson S. L., Faraj S., Kudaravalli S.. Emergence of power laws in online communities: the role of social mechanisms and preferential attachment. MIS Quarterly, 2014, 38(3): 795-808.

[17] Jurca R., Garcin F., Talwar A., Faltings B.. Reporting incentives and biases in online review forums. ACM Transactions on the Web (TWEB), 2010, 4(2): 5:1-5:27.

[18] Kraut, R. E., Resnick, P., Kiesler, S. et al. (2012). Building Successful Online Communities: Evidence-Based Social Design. MIT Press.

[19] Li F., Han C., Huang M., Zhu X., Xia Y. J., Zhang S., Yu H.. Structure-aware review mining and summarization. the 23rd International Conference on Computational Linguistics, 2010 653-661.

[20] Liu B.. Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2010, 2: 627-666.

[21] Liu B.. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167.

[22] Liu Y., Huang X., An A., Yu X.. Modeling and predicting the helpfulness of online reviews. Eighth IEEE International Conference on Data Mining, 2008 443-452.

[23] Mann H. B.. Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 1945 245-259.

[24] Newman M. E.. The structure and function of complex networks. SIAM Review, 2003, 45(2): 167-256.

[25] Pang B., Lee L.. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.

[26] Park D. H., Lee J.. eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 2009, 7(4): 386-398.

[27] Sousa D., Sarmento L., Mendes Rodrigues E.. Characterization of the twitter@ replies network: are user ties social or topical?. In the 2nd International Workshop on Search and Mining User-Generated Contents, 2010 63-70.

[28] Takamura H., Okumura M.. Text summarization model based on maximum coverage problem and its variant. In the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009 781-789.

[29] Titov I., McDonald R.. Modeling online reviews with multi-grain topic models. In the 17th International Conference on World Wide Web, 2008 111-120.

[30] Uren V., Cimiano P., Iria J., Handschuh S., Vargas-Vera M., Motta E., Ciravegna F.. Semantic annotation for knowledge management: requirements and a survey of the state of the art. Web Semantics: Science, Services and Agents on the World Wide Web, 2006, 4(1): 14-28.

[31] Wan X., Zong L., Huang X., Ma T., Jia H., Wu Y., Xiao J.. Named entity recognition in Chinese news comments on the Web, 2011 856-864.

[32] Wang Y., Fesenmaier D. R.. Assessing motivation of contribution in online communities: an empirical investigation of an online travel community. Electronic Markets, 2003, 13(1): 33-45.

[33] Wang Z.. Anonymity, social image, and the competition for volunteers: a case study of the online market for reviews. The BEJournal of Economic Analysis & Policy, 2010, 10(1): 44:1-44:34.

[34] White D. R.. Dynamics of human behavior. Encyclopedia of Complexity and Systems Science, 4608-4631, 2009

[35] Wu F., Huberman B. A.. Opinion formation under costly expression. ACM Transactions on Intelligent Systems and Technology (TIST), 2010, 1(1): 5:1-5:13.

[36] Wu J., Zhu S., Liu H., Xia G.. Cosine interesting pattern discovery. Information Sciences, 2012, 184(1): 176-195.

[37] Yan Q., Yi L., Wu L.. Human dynamic model co-driven by interest and social identity in the MicroBlog community. Physica A: Statistical Mechanics and Its Applications, 2012, 391(4): 1540-1545.

[38] Yu X., Liu Y., Huang X., An A.. Mining online reviews for predicting sales performance: a case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(4): 720-734.

[39] Zhang K., Narayanan R., Choudhary A. N.. Voice of the customers: mining online customer reviews for product feature-based ranking. WOSN, 2010, 10: 11-11.

[40] Zhang Z., Zhang D., Lai J.. urCF: user review enhanced collaborative filtering. 20th Americas Conference on Information Systems, Savannah, Georgia, 2014

[41] Zhou T., Kiet H. A. T., Kim B. J., Wang B. H., Holme P.. Role of activity in human dynamics. EPL (Europhysics Letters), 2008, 82(2): 28002.

[42] Zhu F., Zhang X.. Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 2010, 74(2): 133-148.

[43] Zhuang L., Jing F., Zhu X. Y.. Movie review mining and summarization. the 15th ACM International Conference on Information and Knowledge Management, 2006 43-50.