TY - GEN
T1 - Opinion Spam Detection in Product Reviews Using Self-Training Semi-Supervised Learning Approach
AU - Navastara, Dini Adni
AU - Zaqiyah, Ana Alimatus
AU - Fatichah, Chastine
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - The review of a product can influence a buyer's decision to buy the product. In addition to influencing buyer decisions, fake reviews can also confuse buyers who are looking for product information from honest and genuine reviews. We need a system that can filter spam to reduce the negative influence on product selling and product review writings. Spam that will be detected is the type of brand only spam and not a review. Those types get the initial label through manual labeling. Manual labeling requires a lot of time and effort. Therefore, in this paper, we proposed a self-training semi-supervised learning approach. This method labels spam from the prediction of the labeled training data. The best results were obtained with a scenario without stemming, merging of review centric features and bigram, SMOTE borderline1 oversampling and Polynomial SVM kernel that has accuracy 86.33%.
AB - The review of a product can influence a buyer's decision to buy the product. In addition to influencing buyer decisions, fake reviews can also confuse buyers who are looking for product information from honest and genuine reviews. We need a system that can filter spam to reduce the negative influence on product selling and product review writings. Spam that will be detected is the type of brand only spam and not a review. Those types get the initial label through manual labeling. Manual labeling requires a lot of time and effort. Therefore, in this paper, we proposed a self-training semi-supervised learning approach. This method labels spam from the prediction of the labeled training data. The best results were obtained with a scenario without stemming, merging of review centric features and bigram, SMOTE borderline1 oversampling and Polynomial SVM kernel that has accuracy 86.33%.
KW - Oversampling SMOTE
KW - Review Centric Features
KW - Self Training
KW - Semi-Supervised Learning
KW - Support Vector Machine
KW - bigram
UR - http://www.scopus.com/inward/record.url?scp=85095851347&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA47173.2019.9223407
DO - 10.1109/ICAMIMIA47173.2019.9223407
M3 - Conference contribution
AN - SCOPUS:85095851347
T3 - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding
SP - 169
EP - 173
BT - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019
Y2 - 9 October 2019 through 10 October 2019
ER -