Volume-7 Issue-1, March 2021 – International Journal of Emerging Science and Engineering (IJESE) https://www.ijese.org Exploring Innovation | ISSN: 2319–6378(Online) | A Periodical Journal | Reg. No.: C/1383209 | Published by BEIESP Sat, 25 Sep 2021 06:35:36 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.2 https://www.ijese.org/wp-content/uploads/2020/06/cropped-IJESE-Logo2s-32x32.jpg Volume-7 Issue-1, March 2021 – International Journal of Emerging Science and Engineering (IJESE) https://www.ijese.org 32 32 A2494037121 https://www.ijese.org/portfolio-item/a2494037121/ https://www.ijese.org/portfolio-item/a2494037121/#respond Tue, 14 Sep 2021 09:09:09 +0000 https://www.ijese.org/?post_type=portfolio&p=5128 The International Journal of Emerging Science and Engineering (IJESE) is an open-access, peer-reviewed, monthly international journal with an ISSN 2319-6378 (online). It is published by the Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) periodically in January, February, March, April, May, June, July, August, September, October, November, and December since 2012.
The International Journal of Emerging Science and Engineering (IJESE) aspires to circulate top-notch, peer-reviewed original articles in Engineering and Technology, shrouding Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, and all related interdisciplinary associates.

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]]> The International Journal of Emerging Science and Engineering (IJESE) is an open-access, peer-reviewed, monthly international journal with an ISSN 2319-6378 (online). It is published by the Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) periodically in January, February, March, April, May, June, July, August, September, October, November, and December since 2012.
The International Journal of Emerging Science and Engineering (IJESE) aspires to circulate top-notch, peer-reviewed original articles in Engineering and Technology, shrouding Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, and all related interdisciplinary associates.

Fake Product Monitoring and Removal for Genuine Product FeedbackCROSSMARK Color horizontal
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
Open Access | Ethics and Policies | Cite | Mendeley
© 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.

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