Detecting Online Spams through Supervised Learning Techniques
M.S Minu1, Kamagiri Mounika2, N.Suhasini3, Bezawada Tejaswi4
1M.S Minu*, Assistant Professor, Department Of Computer Science And Engineering, Srmist, Ramapuram, Chennai.
2Kamagiri Mounika, Department Of Computer Science And Engineering, Srmist, Ramapuram, Chennai.
3N.Suhasini, Department Of Computer Science And Engineering, Srmist, Ramapuram, Chennai.
4Bezawada Tejaswi, department Of Computer Science And Engineering, Srmist, Ramapuram, Chennai.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2395-2400 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4252119119/2019©BEIESP | DOI: 10.35940/ijitee.A4252.119119
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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: With more customers utilizing on the online review surveys to educate their administration basic leadership, assessment of reviews which economically affect the reality of organizations. Obviously, crafty people or gatherings have endeavored to manhandle or control online review spam to make benefits, etc, and that tricky recognition and counterfeit sentiment surveys is a subject of continuous research intrigue. In this paper, we clarify how supervised learning strategies are utilized to recognize online spam review surveys, preceding showing its utility utilizing an informational index of lodging reviews.
Keywords: Online Review Surveys, Supervised learning, Unlabeled Data, Naïve Bayes Algorithm, Classifiers, EM Algorithm, Bag of Words, Stop word Filtering, Support Vector Machine classifier.
Scope of the Article: Algorithm Engineering