Spam Detector: A Solution for Finding Bogus Opinions and Spammer Classification in E-Commerce
Nisha Kshirsagar1, Amol Phatak2
1Ms. Nisha Kshirsagar, Dept. of Computer Science & Engineering, N. B. Navale Sinhgad College of Engineering, Solapur, India.
2Prof. Amol Phatak, Dept. of Computer Science & Engineering, N. B. Navale Sinhgad College of Engineering, Solapur, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 2020-2023 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8356038620/2020©BEIESP | DOI: 10.35940/ijrte.F8356.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we propose a system called Spam Detector to find out spam reviews of particular product. This framework uses spam features which helps to review datasets. In proposed system four different categories of features are included such as review behavioral (based on behaviour of review) , user behavioral (based on behaviour of user), review linguistic (based on linguistic pattern of review) and user linguistic (based on linguistic pattern of user). In today’s world, most of people take decision based on information available through social media reviews on a topic or product. Due to this it is possible that anyone can post a review for a particular product which give a chance for spammers to post junk reviews related to particular product and facilities. To find out such spammers and the spam review is becoming important. Some methods exist to cope up with this problem, but these methods hardly detect spam reviews, and are not based on the extracted feature type. Weight of spam features are used to gain better results in the form of different aspects tested on real world review datasets. Here the used dataset is from Amazon websites. The experimental results show that Spam Detector beats the present method.
Keywords: Spam, Review, Feature, Product and Users.
Scope of the Article: Classification.