By a News Reporter-Staff News Editor at NewsRx Law -- Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Uttar Pradesh, India, by NewsRx journalists, research stated, “E-commerce websites are now favourite for shopping comfortably at home without any burden of going to market. Their success depends upon the reviews written by the consumers who used particular products and subsequently shared their experiences with that product.”
The news reporters obtained a quote from the research from the Motilal Nehru National Institute of Technology, “The reviews also affects the buying decision of customer. Because of this reason the activity of fake reviews posting is increasing. The brand competitors of the product or the company itself may involve in posting fraud reviews to gain more profit. Such fraudulent reviews are spam review that badly affects the decision choice of the prospective consumer of the products. Many customers are misguided due to fake reviews. The person, who writes the fake reviews, is called the spammer. Identification of spammers is indirectly helpful in identifying whether the reviews are spam or not. The detection of review spammers is serious concern for the E-commerce business. To help researchers in this vibrant area, we present the state of art approaches for review spammer detection. This paper presents a comprehensive survey of the existing spammer detection approaches describing the features used for individual and group spammer detection, dataset summary with details of reviews, products and reviewers. The main aim of this paper is to provide a basic, comprehensive and comparative study of current research on detecting review spammer using machine learning techniques and give future directions.”
According to the news reporters, the research concluded: “This paper also provides a concise summary of published research to help potential researchers in this area to innovate new techniques.”
For more information on this research see: State-of-art approaches for review spammer detection: a survey. Journal of Intelligent Information Systems , 2018;50(2):231-264. Journal of Intelligent Information Systems can be contacted at: Springer, Van Godewijckstraat 30, 3311 Gz Dordrecht, Netherlands. (Springer - www.springer.com; Journal of Intelligent Information Systems - http://www.springerlink.com/content/0925-9902/)
Our news correspondents report that additional information may be obtained by contacting R.K. Dewang, Motilal Nehru Natl Inst Technol, Dept. of Comp Sci & Engn, Allahabad 211004, Uttar Pradesh, India.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s10844-017-0454-7. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2018, NewsRx LLC
CITATION: (2018-04-19), Findings from Motilal Nehru National Institute of Technology Update Understanding of Machine Learning (State-of-art approaches for review spammer detection: a survey), NewsRx Law, 43, ISSN: 0000-0000, BUTTER® ID: 015482842
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