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

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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.

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References

  1. Aggarwal, C. C. & Zhai, C. (2012). A survey of text clustering algorithms. In: Aggarwal, C. C. & Zhai, C. (eds), Mining Text Data, pp. 77–128. Springer Science & Business Media.

    Chapter  Google Scholar 

  2. Almahairi, A., Kastner, K., Cho, K. & Courville, A. (2015). Learning distributed representations from reviews for collaborative filtering. In the 9th ACM Conference on Recommender Systems, 147–154.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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. (2010). The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5): 944–957.

    Article  Google Scholar 

  7. Cui, H., Mittal, V. & Datar, M. (2006). Comparative experiments on sentiment classification for online product reviews. In AAAI, 6: 1265–1270.

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Gilbert, E. & Karahalios, K. (2010). Understanding deja reviewers. In the 2010 ACM Conference on Computer Supported Cooperative Work, 225–228.

    Google Scholar 

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

    Article  Google Scholar 

  13. Hu, M. & Liu, B. (2004). Mining and summarizing customer reviews. In the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168–177.

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  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. (2010). Structure-aware review mining and summarization. In the 23rd International Conference on Computational Linguistics, 653–661.

    Google Scholar 

  20. Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2: 627–666.

    Google Scholar 

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

    Article  Google Scholar 

  22. Liu, Y., Huang, X., An, A. & Yu, X. (2008). Modeling and predicting the helpfulness of online reviews. In Eighth IEEE International Conference on Data Mining, 443–452.

    Chapter  Google Scholar 

  23. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245–259.

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  28. Takamura, H. & Okumura, M. (2009). 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, 781–789.

    Google Scholar 

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

    Google Scholar 

  30. Uren, V., Cimiano, P., Iria, J., Handschuh, S., Vargas-Vera, M., Motta, E. & Ciravegna, F. (2006). 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, 4(1): 14–28.

    Article  Google Scholar 

  31. Wan, X., Zong, L., Huang, X., Ma, T., Jia, H., Wu, Y. & Xiao, J. (2011). Named entity recognition in Chinese news comments on the Web. In IJCNLP, 856–864.

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  34. White, D. R. (2009). Dynamics of human behavior. In Encyclopedia of Complexity and Systems Science, 4608-4631. Springer New York.

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  40. Zhang, Z., Zhang, D. & Lai, J. (2014). urCF: user review enhanced collaborative filtering. In 20th Americas Conference on Information Systems, Savannah, Georgia, USA, August 7-9.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Zhuang, L., Jing, F. & Zhu, X. Y. (2006). Movie review mining and summarization. In the 15th ACM International Conference on Information and Knowledge Management, 43–50.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Liangqiang Li, Hua Yuan, Yu Qian & Peiji Shao

Authors
  1. Liangqiang Li
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  2. Hua Yuan
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  3. Yu Qian
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  4. Peiji Shao
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Corresponding author

Correspondence to Yu Qian.

Additional information

Liangqiang Li has received the B.S. degree from Sichuan Normal University, Chengdu, in 2004. He is currently a Ph.D. candidate with the Department of Management Science and E-Commerce from University of Electronic Science and Technology of China (UESTC), China. His research interests include business intelligence and E-commerce, information system management, and urban computing.

Hua Yuan received his BS degree in MIS from Tongji University, and the PhD degree in management science and engineering from Tsinghua University, China. He is currently an associate professor of information systems at the University of Electronic Science and Technology of China. His research interests focus on business intelligence and information technology management.

Yu Qian is an associate professor of management science at the University of Electronic Science and Technology of China (UESTC). Her general research interests include operation management and information economics. Her papers have been published and presented in journals and conferences such as the Flexible Services and Manufacturing Journal, Decision Support Systems and POMS annual conference.

Peiji Shao is a professor of information management at the University of Electronic Science and Technology of China. He authored the textbook Management Information System and has published articles in the areas of information management and e-business. In addition to higher education, he is an expert specifically for the Government of Sichuan province of China in the Internet-related sector.

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Li, L., Yuan, H., Qian, Y. et al. Towards exploring when and what people reviewed for their online shopping experiences. J. Syst. Sci. Syst. Eng. 27, 367–393 (2018). https://doi.org/10.1007/s11518-016-5318-0

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  • DOI: https://doi.org/10.1007/s11518-016-5318-0

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