Fake Review Prediction and Review Analysis
Manasi Bansode1, Siddhi Pardeshi2, Suyasha Ovhal3, Pranali Shinde4, Anandkumar Birajdar5
1Manasi Bansode*, Student, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
2Siddhi Pardeshi, Student, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
3Suyasha Ovhal, Student, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
4Pranali Shinde, Student, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
5Anandkumar Birajdar, Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
Manuscript received on May 21, 2021. | Revised Manuscript received on May 28, 2021. | Manuscript published on May 30, 2021. | PP: 143-151 | Volume-10 Issue-7, May 2021 | Retrieval Number: 100.1/ijitee.G90420510721| DOI: 10.35940/ijitee.G9042.0510721
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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: Online reviews can be deceptive or manipulative evaluations of services and products which are often carried out deliberately for manipulation strategy to mislead the readers. Identifying such reviews is an important but challenging problem. There are even some associations in the merchandise industry who are hiring professionals to write fake reviews so that they can promote their products or defame rivals products. Hence we aim to develop a method which will detect fake reviews and remove them. The proposed method classifies users’ reviews into suspicious, fake, positive and negative categories by phase-wise processing. In this paper, we are processing hotel reviews by using different data mining techniques. Moreover the reviews obtained from users are being classified into positive or negative which can be used by a consumer to select a product. Organizations providing services can monitor customer sentiments by scrutinizing and understanding what the customers are thinking about products through reviews. This can help buyers to purchase valuable products and spend their money on quality products. Also in our model end users see star ratings based on reviews for each hotel.
Keywords: Current Feedback Amplifier, Current-mode, Voltage Mode, Multifunction, Inverse Filter, Admittances.