Spam Review Detection in E-Commerce Using Machine Learning

Reviews pose a significant challenge for e-commerce platforms that rely on online reviews to attract customers. The reviews may contain spam that resembles actual genuine reviews while current spam detection methods lack integration of linguistic and behavioural features. In this study, we propose a...

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Published in:6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024
Main Author: Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209649877&doi=10.1109%2fIICAIET62352.2024.10730100&partnerID=40&md5=a28a52d778dfe4e5d295cd1696ed6f06
id 2-s2.0-85209649877
spelling 2-s2.0-85209649877
Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
Spam Review Detection in E-Commerce Using Machine Learning
2024
6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024


10.1109/IICAIET62352.2024.10730100
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209649877&doi=10.1109%2fIICAIET62352.2024.10730100&partnerID=40&md5=a28a52d778dfe4e5d295cd1696ed6f06
Reviews pose a significant challenge for e-commerce platforms that rely on online reviews to attract customers. The reviews may contain spam that resembles actual genuine reviews while current spam detection methods lack integration of linguistic and behavioural features. In this study, we propose a machine learning-based approach for detecting spam reviews using classification techniques. We explore various feature extraction methods, including n-grams and sentiment analysis, to represent the textual content of reviews. We evaluate the performance of three classifiers, including Support Vector Machines (SVM), Random Forest, and Naive Bayes, on a dataset of Shopee reviews. Our results show that SVM performs the best in terms of accuracy, precision, and recall, achieving an F1-score of 0.96. We also investigate the impact of different feature extraction methods on classification performance and find that sentiment analysis provides valuable information for detecting spam reviews. Our approach offers a promising solution for automatically identifying spam reviews, which can help e-commerce to maintain the integrity of their online reputation and enhance customer trust. © 2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
spellingShingle Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
Spam Review Detection in E-Commerce Using Machine Learning
author_facet Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
author_sort Rizali M.N.; Rosli M.M.; Abdullah N.A.S.
title Spam Review Detection in E-Commerce Using Machine Learning
title_short Spam Review Detection in E-Commerce Using Machine Learning
title_full Spam Review Detection in E-Commerce Using Machine Learning
title_fullStr Spam Review Detection in E-Commerce Using Machine Learning
title_full_unstemmed Spam Review Detection in E-Commerce Using Machine Learning
title_sort Spam Review Detection in E-Commerce Using Machine Learning
publishDate 2024
container_title 6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024
container_volume
container_issue
doi_str_mv 10.1109/IICAIET62352.2024.10730100
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209649877&doi=10.1109%2fIICAIET62352.2024.10730100&partnerID=40&md5=a28a52d778dfe4e5d295cd1696ed6f06
description Reviews pose a significant challenge for e-commerce platforms that rely on online reviews to attract customers. The reviews may contain spam that resembles actual genuine reviews while current spam detection methods lack integration of linguistic and behavioural features. In this study, we propose a machine learning-based approach for detecting spam reviews using classification techniques. We explore various feature extraction methods, including n-grams and sentiment analysis, to represent the textual content of reviews. We evaluate the performance of three classifiers, including Support Vector Machines (SVM), Random Forest, and Naive Bayes, on a dataset of Shopee reviews. Our results show that SVM performs the best in terms of accuracy, precision, and recall, achieving an F1-score of 0.96. We also investigate the impact of different feature extraction methods on classification performance and find that sentiment analysis provides valuable information for detecting spam reviews. Our approach offers a promising solution for automatically identifying spam reviews, which can help e-commerce to maintain the integrity of their online reputation and enhance customer trust. © 2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
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language English
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