| Peer-Reviewed

Ensemble Classifiers Employed for Spam Review Detection

Received: 12 June 2021     Accepted: 7 July 2021     Published: 11 August 2021
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Abstract

The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products. Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today. Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection. The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier. The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers. Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.

Published in Engineering Science (Volume 6, Issue 3)
DOI 10.11648/j.es.20210603.11
Page(s) 33-38
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Spam Review, Detection, Ensemble Classifier, Arching Classifier, Weighted Voting

References
[1] N. Jindal, and B. Liu, “Analyzing and detecting review spam,” Seventh IEEE International Conference on Data Mining, 2007, pp. 547-552.
[2] A. A. Benczur, K. Csalogany, T. Sarlos, and M. Uher, “SpamRank– fully automatic link spam detection,” Proceedings of the First International Workshop on Adversarial Information Retrieval on the Web, 2005.
[3] G. Muthukumarasamy, “Spam review detection using a hybrid classification method,” International Journal of Advances in Engineering Sciences, vol. 4, 2014, pp. 22-27.
[4] J. Halloran, “Classification: naive bayes vs logistic regression,” Technical report, University of Hawaii, 2009.
[5] R. Rajat, S. Yirong, Y. N. Andrew, and M. Andrew, “Classification with hybrid generative/discriminative models,” The Annual Conference on Neural Information Processing Systems (NIPS), 2003.
[6] J. Mcauley, and J. Leskovec, “Hidden factors and hidden topics: understanding rating dimensions with review text. ACM Proceedings of the 7th ACM Conference on Recommender Systems, 2013, pp. 165-172.
[7] B. J. Peterson, “Finding a duplicate in a haystack,” Proceedings of the Thirty-first Annual SAS® Users Group, 2006.
[8] Y. Bi, “The Impact of diversity on the accuracy of evidential classifier ensembles,” International Journal of Approximate Reasoning, vol. 53 (4), 2012, pp. 584-607.
[9] P. Melville, and R. J. Mooney, “Constructing diverse classifier ensembles using artificial training examples,” IJCAI Citeseer, 2003, pp. 505-510.
[10] F. Barigou, N. Barigou, and B. Atmani, “Spam detection system combining cellular automata and naïve bayes classifier,” ICWIT Citeseer, 2012, pp. 250-260.
[11] Hussain N, Turab Mirza H, Rasool G, Hussain I, Kaleem M. Spam Review Detection Techniques: A Systematic Literature Review. Applied Sciences, 2019; 9 (5): 987.
[12] Li A, Qin Z, Liu R, Yang Y, Li D. Spam review detection with graph convolutional networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019, pp. 2703-2711.
[13] You Z, Qian T, Liu B. An attribute enhanced domain adaptive model for cold-start spam review detection. In Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 1884-1895.
[14] Hussain N, Mirza HT, Hussain I, Iqbal F, Memon I. Spam review detection using the linguistic and spammer Behavioral methods. IEEE Access, 2020, 8: 53801-16.
[15] Fayaz M, Khan A, Rahman JU, Alharbi A, Uddin MI, Alouffi B. Ensemble Machine Learning Model for Classification of Spam Product Reviews. Complexity, 2020.
[16] Shahariar GM, Biswas S, Omar F, Shah FM, Hassan SB. Spam review detection using deep learning. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, (pp. 0027-0033). IEEE.
Cite This Article
  • APA Style

    Alhassan Jamilu Ibrahim, Maheyzah Siraj, Usman Abubakar Jauro. (2021). Ensemble Classifiers Employed for Spam Review Detection. Engineering Science, 6(3), 33-38. https://doi.org/10.11648/j.es.20210603.11

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    ACS Style

    Alhassan Jamilu Ibrahim; Maheyzah Siraj; Usman Abubakar Jauro. Ensemble Classifiers Employed for Spam Review Detection. Eng. Sci. 2021, 6(3), 33-38. doi: 10.11648/j.es.20210603.11

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    AMA Style

    Alhassan Jamilu Ibrahim, Maheyzah Siraj, Usman Abubakar Jauro. Ensemble Classifiers Employed for Spam Review Detection. Eng Sci. 2021;6(3):33-38. doi: 10.11648/j.es.20210603.11

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  • @article{10.11648/j.es.20210603.11,
      author = {Alhassan Jamilu Ibrahim and Maheyzah Siraj and Usman Abubakar Jauro},
      title = {Ensemble Classifiers Employed for Spam Review Detection},
      journal = {Engineering Science},
      volume = {6},
      number = {3},
      pages = {33-38},
      doi = {10.11648/j.es.20210603.11},
      url = {https://doi.org/10.11648/j.es.20210603.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.es.20210603.11},
      abstract = {The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products. Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today. Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection. The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier. The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers. Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Ensemble Classifiers Employed for Spam Review Detection
    AU  - Alhassan Jamilu Ibrahim
    AU  - Maheyzah Siraj
    AU  - Usman Abubakar Jauro
    Y1  - 2021/08/11
    PY  - 2021
    N1  - https://doi.org/10.11648/j.es.20210603.11
    DO  - 10.11648/j.es.20210603.11
    T2  - Engineering Science
    JF  - Engineering Science
    JO  - Engineering Science
    SP  - 33
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2578-9279
    UR  - https://doi.org/10.11648/j.es.20210603.11
    AB  - The advancement of technology and the use of internet have changed many aspects of human culture over the years. Today, consumers take confidence in e-commerce platforms like amazon and eBay for comprehensive understanding of products and services when making a purchase decision. Here the web or user-generated content from consumers of such products and services, known as reviews, are exploited by spam reviewers to falsely promote or downgrade some targeted products. Despite potential solutions, Identifying and preventing review spam are still one of the top challenges faced by web search engines today. Therefore, in the quest to provide a more improved and efficient classification of review spam, this research probed different techniques in order to find most effective solution to spam detection. The research employed three base classifiers, Naïve Bayes, Support Vector Machines and Logistic Regression to form ensemble classifiers complimented with Arching classifier. The Arching classifier performs the weighted voting that produces the final class label with performance and accuracy higher than either of the individual base classifiers. Cross-validation is used as evaluation metrics to measure the performance or effectiveness of the ensemble classifiers while the experimental results shows that the ensemble classifiers achieve the best results compared to the single based classifier in terms of Precision, Recall, F1-measure and Accuracy.
    VL  - 6
    IS  - 3
    ER  - 

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Author Information
  • Information Assurance and Security Research Group (IASRG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai Johor, Malaysia

  • Information Assurance and Security Research Group (IASRG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai Johor, Malaysia

  • Information Assurance and Security Research Group (IASRG), Faculty of Computing, Universiti Teknologi Malaysia, Skudai Johor, Malaysia

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