Open AccessFake Review Prediction and Review Analysis
Author(s) -
Manasi Bansode,
Siddhi Pardeshi,
Suyasha Ovhal,
Pranali Shinde,
Anandkumar Birajdar
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g9042.0510721
Subject(s) - product (mathematics) , quality (philosophy) , computer science , sentiment analysis , marketing , business , advertising , data science , artificial intelligence , philosophy , geometry , mathematics , epistemology
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.