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 |
Spam Review, Detection, Ensemble Classifier, Arching Classifier, Weighted Voting
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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
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
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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Download@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}
}
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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