Advertisement

A Sentiment-Based Trust and Reputation System in E-Commerce by Extending SentiWordNet

  • Hasnae RahimiEmail author
  • Abdellatif Mezrioui
  • Najima Daoudi
Conference paper
  • 115 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076)

Abstract

Trust is a decisive factor in e-services and especially in e-commerce. E-customers usually rely on others’ opinions, reviews, recommendations on products, and services to make the right purchase decision. Nevertheless, deceptive reviewers deliberately disseminate fake and dishonest reviews to falsify the products’ reputation. Consequently, there is a need for Trust and Reputation Assessment to aggregate these text reviews and compute their related reputation scores. For this purpose, Natural Language Processing cannot be omitted from the process of generating reputation scores. In this paper, we propose a Trust and Reputation System named SentiTrustCom STC which is composed of two subsystems: (1) A Combined Idiomatic Ontology-based Sentiment Orientation System that employs NLP techniques and extends SentiWordNet to analyze Text reviews and compute their related Sentiment orientation scores; (2) Trust and Reputation Engine that proposes algorithms to generate reliable Trust and Reputation scores using the generated Sentiment Polarities as inputs. STC aims to analyze the users’ behavioral intention in order to detect any ill-intentioned interventions that could falsify the products’ reputation and hence distort the overall trust among reviewers.

Keywords

Soft security Trust and reputation systems Sentiment mining Machine learning Behavioral intention detection SentiWordNet Natural language processing Lexical approach E-commerce 

References

  1. 1.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Proc. Assoc. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  2. 2.
    Sharma, R., Nigam, S., Jain, R.: Mining of product reviews at aspect level. Proc. Int. J. Found. Comput. Sci. Technol. (IJFCST) 4(3) (2014)Google Scholar
  3. 3.
    Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. In: Proc. Int. J. Multimedia Ubiquit. Eng. 10, 215–230. http://dx.doi.org/10.14257/ijmue21. ISSN: 1975-0080
  4. 4.
    Hamouda, A., Rohaim, M.: Reviews classification using SentiWordNet Lexicon. Proc. Online J. Comput. Sci. Inf. Technol. (OJCSIT) 2(1)Google Scholar
  5. 5.
    Vinodhini, G., Chandrasekaran, R.: Sentiment analysis and opinion mining: a survey. Proc. Int. J. Adv. Res. Comput. Sci. Softw. Eng. Res. Pap. 2(6) (2012). ISSN: 2277 128X. Available online at: www.ijarcsse.com
  6. 6.
    Kim, H., Song, M.: An ontology-based approach to sentiment classification of mixed opinions in online restaurant reviews. In: The Proceedings of the 5th International Conference, SocInfo 2013, Kyoto, Japan, pp. 95–108, 25–27 Nov 2013Google Scholar
  7. 7.
    Phyu, K., Shein, P.: Ontology based combined approach for sentiment classification. In: The Proceedings of the 3rd International Conference on Communications and information technology, pp. 112–115 (2013)Google Scholar
  8. 8.
    Rahimi, H., El bakkali, H.: State of the art of trust and reputation systems in e-commerce context. Proc. IJCSI Int. J. Comput. Sci. Issues 14(3) (2017)Google Scholar
  9. 9.
    Rahimi, H., Elbakkali, H.: CIOSOS: combined idiomatic ontology based sentiment orientation system for trust reputation in e-commerce. In: The Proceedings of the International Joint Conference Volume 369 of the series Advances in Intelligent Systems and Computing. International Conference Category B, Springer International Publishing, pp. 189–200, 27 May 2015Google Scholar
  10. 10.
    Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06), pp. 417–422Google Scholar
  11. 11.
    Abdel-Hafez, A., Xu, Y., Tjondronegoro, D.: Product reputation model: an opinion mining based approach. In: The Proceedings of the 1st International Workshop on Sentiment Discovery from Affective Data (SDAD 2012), CEUR Workshop Proceedings, Bristol, pp. 16–27Google Scholar
  12. 12.
    Wang, H.., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: The Proceedings of the 16th ACM SIGKDD International Conference on KDD, New York, NY, USA, pp. 783–792 (2010)Google Scholar
  13. 13.
    Lu, Y., Zhai, C., Sundaresan, N.: Rated aspect summarization of short comments. In: The Proceedings of the International World Wide Web Conference Committee (IW3C2). WWW 2009. ACM, Madrid, Spain. 978-1-60558-487-4/09/04. 20–24 Apr 2009Google Scholar
  14. 14.
    Cho, J., Kwon, K., Park, Y.: Q-rater: a collaborative reputation system based on source credibility theory. Proc. Expert Syst. Appl. 36, 3751–3760 (2009)CrossRefGoogle Scholar
  15. 15.
    Leberknight, S., Sen, S., Chiang, M.: On the volatility of online ratings: an empirical study. In: The Proceeding of the E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life, vol. 108, pp. 77–86. Springer Berlin Heidelberg (2012)Google Scholar
  16. 16.
    Saenger, J., Günther, P.: Interactive reputation systems: how to cope with malicious behavior in feedback mechanisms. In: The Proceedings of the Business & Information Systems Engineering. Springer.  https://doi.org/10.1007/s12599-017-0493-1 (2017)
  17. 17.
    Firake, V.R., Patil, Y.S.: Survey on CommTrust: multi-dimensional trust using mining e-commerce feedback comments. Proc. Int. J. Innovative Res. Comput. Commun. Eng. IJIRCCE 3(3).  https://doi.org/10.15680/ijircce.2015.03030371640 (2015)
  18. 18.
  19. 19.
    King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)Google Scholar
  20. 20.
    Dhillon, P., Foster, D., Ungar, L.: Eigenwords: spectral word embeddings. J. Mach. Learn. Res. (JMLR) 16 (2015)Google Scholar
  21. 21.
    Joachims, T., Finley, T., Yu, C.N.: Cutting-plane training of structural SVMs. Mach. Learn. 77(1), 27–59 (2009)CrossRefGoogle Scholar
  22. 22.
  23. 23.
  24. 24.
  25. 25.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)Google Scholar
  26. 26.
    Zhang, Y., Zhang, M., Liu, Y., Ma, S.: Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification. In: The Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, ACM, New York, NY, USA, pp. 1027–1030 (2014)Google Scholar
  27. 27.
    Christiane, F.: WordNet: an electronic lexical database. In: The Proceedings of the MIT Press, Cambridge, MA (1998)Google Scholar
  28. 28.
    Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. In: Proc. Int. J. Multimedia Ubiquit. Eng. 10, 215–230. http://dx.doi.org/10.14257/ijmue21. ISSN: 1975-0080 (2015)
  29. 29.
    Lee, S.-J., Ahn, C., Song, K.M., Ahn, H.: Trust and distrust in e-commerce. Sustainability 10, 1015 (2018)CrossRefGoogle Scholar
  30. 30.
    Raj, E.D., Dhinesh Babu, L.D.: An enhanced trust prediction strategy for online social networks using probabilistic reputation features, NeuroComputing Elsevier 219, 412–421 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Hasnae Rahimi
    • 1
    • 2
    Email author
  • Abdellatif Mezrioui
    • 2
  • Najima Daoudi
    • 1
  1. 1.Lyrica Research Team, Ecole des Sciences de l’information ESIRabatMorocco
  2. 2.RAISS Research TeamInstitut National des Postes et des Télécommunication INPTRabatMorocco

Personalised recommendations