Open AccessFake Detection of Online Reviews using Semi-Supervised and Supervised Learning
Author(s) -
Aneel Narayanapur,
Pavankumar Naik,
G Suraksha,
S I Pavitra,
Shruddha Mudigoudar,
Megha Honnali
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit2063112
Subject(s) - computer science , supervised learning , product (mathematics) , set (abstract data type) , data science , online learning , semi supervised learning , artificial intelligence , machine learning , world wide web , mathematics , artificial neural network , geometry , programming language
Online reviews have great impact on today's business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interests. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on data set containing hotel reviews.