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Reputation Systems: Evaluating Reputation Among All Good Sellers

Abstract

A reputation system assists people selecting whom to trust, encourages trustworthy action, and discourages participation of unskilled or dishonest. The “all good reputation” problem is common in current reputation systems, especially in e-commerce domain, making it difficult for buyers to choose credible sellers. Observing high growth of online data in Hindi language, in this paper, we propose a reputation system in this language. The functions of this system include (1) review mining for different criteria of online transactions, (2) calculation of reputation rating using Bayesian method, (3) calculation of reputation weight using typed dependency relation representation and Latent Dirichlet Allocation topic modeling technique for each criteria from user reviews, and (4) ranking sellers based on computed reputation score. Extensive simulations conducted on eBay dataset and TripAdvisor dataset show its effectiveness in solving “all good reputation” problem. So far, as our knowledge is concerned, this is the first work in Hindi language on reputation system.

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Notes

  1. https://www.amazon.in/gp/help/customer/display.html

  2. http://pages.ebay.in/help/feedback/allaboutfeedback.html

  3. http://en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers

  4. http://www.news18.com/news/tech/hindi-content-consumption-on-internet-growing-at-94-1-in-5-indian-users-prefer-hindi-google-1047247.html

  5. http://www.business-standard.com/article/current-affairs/hindi-internet-users-estimated-at-60-million-in-india-survey-116020400922_1.html

  6. http://www.internetworldstats.com/top20.htm

  7. http://trak.in/tags/business/2015/08/19/hindi-content-content-consumption-growth-india-google/

  8. http://www.internetworldstats.com/stats7.htm

  9. http://sivareddy.in/downloads#hindi_tools

  10. http://sivareddy.in/downloads#hindi-dependency-parser

  11. https://translate.google.com/

  12. http://pages.ebay.in/services/forum/feedback.html

References

  1. D.M. Blei, A.Y. Ng, M.I. Jordan, Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  2. J. Blitzer, M. Dredze, F. Pereira, Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification, in Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, (2007), pp. 440–447

    Google Scholar 

  3. E. Cambria, B. Schuller, Y. Xia, C. Havasi, New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)

    Article  Google Scholar 

  4. N. Chen, J. Lin, S.C. Hoi, X. Xiao, B. Zhang, Ar-miner: mining informative reviews for developers from mobile app marketplace, in Proceedings of the 36th International Conference on Software Engineering (ACM, 2014), pp. 767–778

  5. D. Coetzee, A. Fox, M.A. Hearst, B. Hartmann, Should your mooc forum use a reputation system? in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (ACM, 2014), pp. 1176–1187

  6. A. Das, S. Bandyopadhyay, Sentiwordnet for Indian languages. Asian Federation for Natural Language Processing, China, pp. 56–63 (2010)

  7. M.C. De Marneffe, B. MacCartney, C.D. Manning, et al., Generating typed dependency parses from phrase structure parses, in Proceedings of LREC, Vol. 6, (2006), pp. 449–454

    Google Scholar 

  8. M.C. De Marneffe, C.D. Manning, The stanford typed dependencies representation, in Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation (Association for Computational Linguistics, 2008), pp. 1–8

  9. E. Ert, A. Fleischer, N. Magen, Trust and reputation in the sharing economy: the role of personal photos in airbnb. Tour. Manage. 55, 62–73 (2016)

    Article  Google Scholar 

  10. M. Gamon, Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis, in Proceedings of the 20th International Conference on Computational Linguistics (Association for Computational Linguistics, 2004), p. 841

  11. T.L. Griffiths, M. Steyvers, Finding scientific topics. Proc. Natl. Acad. Sci. 101(suppl 1), 5228–5235 (2004)

    Article  Google Scholar 

  12. G. Heinrich, Parameter Estimation for Text Analysis. University of Leipzig, Tech. Rep (2008)

  13. R. Herbrich, T. Graepel, D. Shaw, Reputation system. US Patent 8,374,973 (2013)

  14. Y. Hijikata, H. Ohno, Y. Kusumura, S. Nishida, Social summarization of text feedback for online auctions and interactive presentation of the summary. Knowl.-Based Syst. 20(6), 527–541 (2007)

    Article  Google Scholar 

  15. M. Hu, B. Liu, Mining and summarizing customer reviews, in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2004), pp. 168–177

  16. V. Jha, N. Manjunath, P. Deepa Shenoy, K.R. Venugopal, Hsas: Hindi subjectivity analysis system, in 2015 Annual IEEE India Conference (INDICON) (IEEE, 2015), pp. 1–6

  17. V. Jha, N. Manjunath, P. Deepa Shenoy, K.R. Venugopal, Hsra: Hindi stopword removal algorithm, in International Conference on Microelectronics, Computing and Communications (MicroCom), 2016 (IEEE, 2016), pp. 1–5

  18. V. Jha, N. Manjunath, P. Deepa Shenoy, K.R. Venugopal, Sentiment analysis in a resource scarce language: Hindi. International Journal of Scientific and Engineering Research. 7(9), 968–980 (2016)

    Google Scholar 

  19. V. Jha, N. Manjunath, P. Deepa Shenoy, K.R. Venugopal, L.M. Patnaik, Homs: Hindi opinion mining system, in IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015 (IEEE, 2015), pp. 366–371

  20. V. Jha, R. Savitha, P. Deepa Shenoy, K.R. Venugopal, Reputation system: Evaluating reputation among all good sellers, in Proceedings of NAACL-HLT, (2016), pp. 115–121

  21. V. Jha, R. Savitha, S.S. Hebbar, P. Deepa Shenoy, K.R. Venugopal, Hmdsad: Hindi multi-domain sentiment aware dictionary, in 2015 International Conference on Computing and Network Communications (CoCoNet) (IEEE, 2015), pp. 241–247

  22. V. Jha, G.R. Shreedevi, P. Deepa Shenoy, K.R. Venugopal, Generating multilingual subjectivity resources using english language. Int. J. Comput. Appl. 152(9), 41–47 (2016). doi:10.5120/ijca2016911946. http://www.ijcaonline.org/archives/volume152/number9/26362-2016911946

    Google Scholar 

  23. A. Jøsang, Robustness of trust and reputation systems: Does it matter? (Springer, Berlin, 2012), pp. 253–262

    Google Scholar 

  24. A. Jøsang, R. Ismail, C. Boyd, A survey of trust and reputation systems for online service provision. Decis. Support. Syst. 43(2), 618–644 (2007)

    Article  Google Scholar 

  25. A. Jsang, R. Ismail, The beta reputation system, in Proceedings of the 15th Bled Electronic Commerce Conference, Vol. 5, (2002), pp. 2502–2511

    Google Scholar 

  26. R. Jurca, B. Faltings, An incentive compatible reputation mechanism, in IEEE International conference on e-commerce, 2003. CEC 2003 (IEEE, 2003), pp. 285–292

  27. D. Kang, Y. Park, Review-based measurement of customer satisfaction in mobile service: sentiment analysis and Vikor approach. Expert Syst. Appl. 41(4), 1041–1050 (2014)

    Article  Google Scholar 

  28. Y.A. Kim, M.A. Ahmad, Trust, distrust and lack of confidence of users in online social media-sharing communities. Knowl.-Based Syst. 37, 438–450 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. B. Liu, L. Zhang, A Survey of Opinion Mining and Sentiment Analysis (Springer, USA, 2012), pp. 415–463

  31. X. Liu, A. Datta, K. Rzadca, Trust beyond reputation: a computational trust model based on stereotypes. Electron. Commer. Res. Appl. 12(1), 24–39 (2013)

    Article  Google Scholar 

  32. Y. Lu, C. Zhai, N. Sundaresan, Rated aspect summarization of short comments, in Proceedings of the 18th International Conference on World Wide Web (ACM, 2009), pp. 131–140

  33. N.H. Miller, P. Resnick, R.J. Zeckhauser, Eliciting honest feedback in electronic markets. Social Science Research Network Electronic Journal (2002)

  34. D. Movshovitz-Attias, Y. Movshovitz-Attias, P. Steenkiste, C. Faloutsos, Analysis of the reputation system and user contributions on a question answering website: Stackoverflow, in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2013, (2013), pp. 886–893

  35. A.K. Nassirtoussi, S. Aghabozorgi, T.Y. Wah, D.C.L. Ngo, Text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014)

    Article  Google Scholar 

  36. J. O’Donovan, B. Smyth, V. Evrim, D. McLeod, Extracting and visualizing trust relationships from online auction feedback comments, in IJCAI, (2007), pp. 2826–2831

    Google Scholar 

  37. B. Pang, L. Lee, Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008)

    Article  Google Scholar 

  38. G. Qiu, B. Liu, J. Bu, C. Chen, Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)

    Article  Google Scholar 

  39. P. Resnick, R. Zeckhauser, Trust among strangers in internet transactions: empirical analysis of ebay’s reputation system. The Economics of the Internet and E-commerce. 11(2), 23–25 (2002)

    Google Scholar 

  40. P. Resnick, R. Zeckhauser, E. Friedman, K. Kuwabara, Reputation systems: facilitating trust in internet interactions. Working paper, mimeo. Commun ACM (2001)

  41. P. Resnick, R. Zeckhauser, J. Swanson, K. Lockwood, The value of reputation on ebay: a controlled experiment. Exp. Econ. 9(2), 79–101 (2006)

    Article  Google Scholar 

  42. J. Serrano-Guerrero, J.A. Olivas, F.P. Romero, E. Herrera-Viedma, Sentiment analysis: a review and comparative analysis of web services. Inform. Sci. 311, 18–38 (2015)

    Article  Google Scholar 

  43. P. D. Turney, Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews, in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (Association for Computational Linguistics, 2002), pp. 417–424

  44. O.A. Wahab, J. Bentahar, H. Otrok, A. Mourad, A survey on trust and reputation models for web services: single, composite, and communities. Decis. Support. Syst. 74, 121–134 (2015)

    Article  Google Scholar 

  45. H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis on review text data: a rating regression approach, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2010), pp. 783–792

  46. H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis without aspect keyword supervision, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2011), pp. 618–626

  47. W.T. Wang, Y.S. Wang, E.R. Liu, The stickiness intention of group-buying websites: the integration of the commitment-trust theory and e-commerce success model. Inf. Manag. 53(5), 625–642 (2016)

    Article  Google Scholar 

  48. X. Wang, L. Liu, J. Su, Rlm: a general model for trust representation and aggregation. IEEE Trans. Serv. Comput. 5(1), 131–143 (2012)

    Article  Google Scholar 

  49. A. Wierzbicki, T. Kaszuba, R. Nielek, P. Adamska, A. Datta, Improving computational trust representation based on internet auction traces. Decis. Support. Syst. 54(2), 929–940 (2013)

    Article  Google Scholar 

  50. S. Xiao, M. Dong, Hidden semi-markov model-based reputation management system for online to offline (o2o) e-commerce markets. Decis. Support. Syst. 77, 87–99 (2015)

    Article  Google Scholar 

  51. S.R. Yan, X.L. Zheng, Y. Wang, W.W. Song, W.Y. Zhang, A graph-based comprehensive reputation model: exploiting the social context of opinions to enhance trust in social commerce. Inf. Sci. 318, 51–72 (2015). Security, privacy and trust in network-based big data

    MathSciNet  Article  Google Scholar 

  52. C.W. Yoo, Y.J. Kim, G.L. Sanders, The impact of interactivity of electronic word of mouth systems and e-quality on decision support in the context of the e-marketplace. Inf. Manag. 52(4), 496–505 (2015). doi:10.1016/j.im.2015.03.001

    Article  Google Scholar 

  53. X. Zhang, L. Cui, Y. Wang, Commtrust: computing multi-dimensional trust by mining e-commerce feedback comments. IEEE Trans. Knowl. Data Eng. 26(7), 1631–1643 (2014)

    Article  Google Scholar 

  54. Y. Zhang, J. Bian, W. Zhu, Trust fraud: a crucial challenge for China’s e-commerce market. Electron. Commer. Res. Appl. 12(5), 299–308 (2013). doi:10.1016/j.elerap.2012.11.005. Chinese E-Commerce

    Article  Google Scholar 

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

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Jha, V., Ramu, S., Shenoy, P.D. et al. Reputation Systems: Evaluating Reputation Among All Good Sellers. Data-Enabled Discov. Appl. 1, 8 (2017). https://doi.org/10.1007/s41688-017-0008-8

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  • DOI: https://doi.org/10.1007/s41688-017-0008-8

Keywords

  • E-commerce
  • Hindi
  • Natural language processing
  • Reputation system
  • Review mining