Fake Product Monitoring and Removal for Genuine Product Feedback
Mayuri Patil1, Snehal Nikumbh2, Aparna Parigond3, Madhavi Patil4
1Mayuri Manikrao Patil*, Department of Computer Engineering, Dr. D.Y. Patil School of Engineering Academy, Pune (M.H), India.
2Snehal Nimba Nikumbh, Department of Computer Engineering, Dr. D.Y. Patil School of Engineering Academy, Pune (M.H), India.
3Aparna Parshwanath Parigond, Department of Computer Engineering, Dr. D.Y. Patil School of Engineering Academy, Pune (M.H), India.
4Madhavi Patil Kothari, Department of Computer Engineering, Dr. D.Y. Patil School of Engineering Academy, Pune (M.H), India.
Manuscript received on March 19 , 2021. | Revised Manuscript received on March 26, 2021. | Manuscript published on March 30, 2021. | PP: 1-3 | Volume-7 Issue-1, March 2021. | Retrieval Number: 100.1/ijese.A2494037121 | DOI: 10.35940/ijese.A2494.037121
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© The Authors. Published By: 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: A customer’s decision to purchase a product or service are primarily influenced by online reviews. Customers use online reviews, which are valuable sources of information to understand the public opinion on products and/or services. Dependability on online reviews can give rise to the potential concern that violator could give deceitful reviews in order to synthetically promote or decry products and services. This practice is known as Opinion Spam, where spammers manipulate reviews by making fake, untruthful, or deceptive reviews to get profit and boost their products, and devalue a competitor’s products. In order to tackle this issue, we propose to build a fraud risk management system and removal model. This captures fraudulent transactions based on user behaviors and network, analyses them in real-time using Data Mining, and accurately predicts the suspicious users and transactions. In this system, we use two algorithms NLP and TF-IDF to differentiate between fake and genuine reviews or feedback received by the customers.
Keywords: Genuine Reviews, Fake Reviews, Opinion Spam, Opinion Mining.