Abstract
In the context of the explosive growth of online reviews in e-commerce, some merchants began to hire consumers to make fake purchases to increase sales, which caused the difficulty in identifying fake reviews. Therefore, from the perspective of consumers’ shopping behavior, a feature recognition method based on comment text, reviewer attribute and reviewer’s shopping behavior (referred to as TAB) is proposed to solve the problem of fake reviews caused by fake shopping behavior. Compared with BOW, TF-IDF, N-gram, FSP and other traditional feature extraction methods, experiments were carried out on Naive Bayes, SVM, Logistic Regression, Random Forest, CNN and other classification models. By changing the data dimension, the stability of each false comment recognition model was analyzed. The experimental results show that the TAB feature recognition method has better classification performance on logistic regression and support vector machine models, and the model is relatively stable, and the classification effect is not easily affected by dimension change.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of Computational Methods in Sciences and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.