A Survey on Review Spam Detection Methods using Deep Learning Approach

سال انتشار: 1401
محل انتشار: فصلنامه بین المللی وب پژوهی، دوره: 5، شماره: 1
کد COI اختصاصی: JR_IJWR-5-1_003
زبان مقاله: انگلیسی
تعداد مشاهده: 169

نویسندگان

Master of Information Technology Engineering, Deep Learning Research Lab, Faculty of Engineering, College of Farabi, University of Tehran, Iran

Kazim Fouladi

Assistant Professor, Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran; kfouladi@ut.ac.ir

چکیده

Review spam is an opinion written to promote or demote a product or brand on websites and other internet services by some users. Since it is not easy for humans to recognize these types of opinions, a model can be provided to detect them. In recent years, much research has been done to detect these types of reviews, and with the expansion of deep neural networks and the efficiency of these networks in various issues, in recent years, multiple types of deep neural networks have been used to identify spam reviews. This paper reviews the proposed deep learning methods for the problem of review spam detection. Challenges, evaluation criteria, and datasets in this area are also examined.

کلیدواژه ها

Review Spam Detection, Opinion Spam, Deep Learning, convolutional neural network, Long Short-Term Memory (LSTM), Literature Survey

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