Detection of Fake Online Reviews using Semi-Supervised and Supervised Learning

YEAR : 2019

Category: Tags: ,


Technologies are changing rapidly. Old technologies are continuously being replaced by new and sophisticated ones. These new technologies are enabling people to have their work done efficiently. Such an evolution of technology is online marketplace. Some approaches are review content based and some are based on behaviour of the user who is posting reviews. Content based study focuses on what is written on the review that is the text of the review where user behaviour-based method focuses on country, IP-address, number of posts of the reviewer etc. Most of the proposed approaches are supervised classification models. Few researchers, also have worked with semi-supervised models. Semi-supervised methods are being introduced for lack of reliable labelling of the reviews.Online reviews have great impact on today’s business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interests.



System : Intel i3 and above
Hard Disk : 40GB
RAM : Minimum 4GB
Processer : 64-bit, four-core, 2.5 GHz minimum per core


Front End Language : HTML, CSS, JAVA, JSP SERVELTS
Backend : My SQL
Operating System : Windows 10 or 11


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