Open AccessResearch Paper on Fake Online Reviews Detection using Semi-supervised and Supervised learning
Author(s) -
Ajanta Chettri,
Amal George,
A. Rengarajan,
Feon Jaison
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41687
Subject(s) - product (mathematics) , supervised learning , computer science , task (project management) , data science , marketing , artificial intelligence , business , economics , management , geometry , mathematics , artificial neural network
Today's business and commerce are heavily influenced by online reviews. Most online product purchase decisions are based on customer reviews. As a result, opportunistic individuals or groups seek to shake product reviews in their favor. Fake online reviews have a significant impact on the efficiency of online consumers, merchants and e-commerce markets. Despite academic efforts to study fake reviews, there remains a need for research that can systematically analyze and summarize their causes and consequences. This task provides a semi-supervised and supervised text mining model for detecting fake web reviews and comparing their effectiveness to hotel review datasets. Keywords: Semi-Supervised. Supervised, Detection, Fake Review, Marketing