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Product review management software based on multiple classifiers

Product review management software based on multiple classifiers

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In recent years, due to significant developments in online shopping and the widespread use of e-commerce, competition among companies has increased considerably. As a result, product reviews have become a primary factor in consumers' decision making, which has given rise to a market for fraudulent reviews about real products and services. In this study, the authors propose a model using a multiple classifier system to identify deceptive negative customer reviews, which they validated with a dataset of hotel reviews from TripAdvisor. The proposed model used five classifiers by following the majority voting combination rule – namely, libLinear, libSVM, sequential minimal optimisation, random forest, and J48 – the first two of which represent different implementations of support vector machines. Ultimately, the model provided remarkable results that demonstrate improvement upon approaches reported in the literature.

References

    1. 1)
      • 1. Jindal, N., Liu, B.: ‘Review spam detection’. 16th Int. Conf. on World Wide Web, New York, NY, USA, 2007, pp. 11891190.
    2. 2)
      • 2. Broder, A.: ‘On the resemblance and containment of documents’. Compression and Complexity of Sequences 1997: Proc., Salerno, Italy, 11–13 June 1997.
    3. 3)
      • 3. Jindal, N., Liu, B.: ‘Opinion spam and analysis’. 2008 Int. Conf. on Web Search and Data Mining, New York, NY, USA, 2008, pp. 219230.
    4. 4)
      • 4. Ott, M.: ‘Deceptive opinion spam corpus v1.4’, Available at: http://myleott.com/op_spam, accessed: 03 August 2015.
    5. 5)
      • 5. Ott, M., Choi, Y., Cardie, C., et al: ‘Finding deceptive opinion spam by any stretch of the imagination’. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.
    6. 6)
      • 6. Ott, M., Cardie, C., Hancock, J.: ‘Estimating the prevalence of deception in online review communities’. 21st Int. Conf. on World Wide Web, New York, NY, USA, 2012, pp. 201210.
    7. 7)
      • 7. Ott, M., Cardie, C., Hancock, J.: ‘Negative deceptive opinion spam’. Proc. of the 2013 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, GA, USA, 2013.
    8. 8)
      • 8. Lau, R.Y.K., Liao, S.Y., Kwok, R.C.W., et al: ‘Text mining and probabilistic language modeling for online review spam detection’, ACM Trans. Manage. Inf. Syst., 2012, 2, (4), pp. 130, (Article no 25).
    9. 9)
      • 9. Sharma, K., Lin, K.-I.: ‘Review spam detector with rating consistency check’. 51st ACM Southeast Conf., New York, NY, 2013.
    10. 10)
      • 10. Chandy, R., Gu, H.: ‘Identifying spam in the iOS app store’. 2nd Joint WICOW/AIRWeb Workshop on Web Quality, New York, NY, 2012, pp. 5659.
    11. 11)
      • 11. Morales, A., Sun, H., Yan, X.: ‘Synthetic review spamming and defense’. 22nd Int. Conf. on World Wide Web. Int. World Wide Web Conf.s Steering Committee, Geneva, Switzerland, 2013, pp. 155156.
    12. 12)
      • 12. Sun, H., Morales, A., Yan, X.: ‘Synthetic review spamming and defense’. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2013, pp. 10881096.
    13. 13)
      • 13. Mukherjee, A., Kumar, A., Liu, B., et al: ‘Spotting opinion spammers using behavioral footprints’. 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2013, pp. 632640.
    14. 14)
      • 14. Xie, S., Wang, G., Lin, S., et al: ‘Review spam detection via temporal pattern discovery’. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2012, pp. 823831.
    15. 15)
      • 15. Jindal, N., Liu, B., Lim, E.P.: ‘Finding unusual review patterns using unexpected rules’. Proc. of the 19th ACM Int. Conf. on Information and Knowledge Management, 2010.
    16. 16)
      • 16. Li, H., Chen, Z., Mukherjee, A., et al: ‘Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns’. Int. AAAI Conf. on Web and Social Media (ICWSM), 2015, pp. 634637.
    17. 17)
      • 17. Li, H., Chen, Z., Liu, B., et al: ‘Spotting fake reviews via collective positive-unlabeled learning’. Int. Conf. on IEEE Data Mining (ICDM), 2014, 2014, pp. 899904.
    18. 18)
      • 18. Lim, E.P., Nguyen, V.A., Jindal, N., et al: ‘Detecting product review spammers using rating behaviors’. Proc. of the 19th ACM Int. Conf. on Information and Knowledge Management, 2010, pp. 939948.
    19. 19)
      • 19. Atasoy, H.: ‘ARFF creator for text classification’. Available at: http://www.atasoyweb.net/Metin-Siniflandirma-Icin-ARFFOlusturucu, accessed: 03 September 2015.
    20. 20)
      • 20. Dietterich, T.G.: ‘Ensemble methods in machine learning’. Int. Workshop on Multiple Classifier Systems, Berlin Heidelberg, 2000.
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