Detection and analysis of fake reviews on online service portal - Liong, Yong Xuan (2022)
Nowadays, the use of the World Wide Web and online service platforms has been quite popular, especially during the Covid-19 outbreak, which resulted in the implementation of lockdown, social isolation, and other preventive measures across the country. Massive amounts of products and services are offered through online platforms, leading to a significant volume of information being generated. Consumers can also provide reviews on products or services that they have purchased on online shopping platforms. In order to reach a conclusion on business strategies and product or service improvements, these reviews are beneficial to both consumers and firm alike. Some businesses, on the other hand, are recruiting writers to post fraudulent favourable impressions about their own products or services, or dishonest bad comments about their rivals' products or services, in exchange for a fee. This strategy provides incorrect information to new customers who are looking to purchase such things or services, and as a result, a system that can identify and eliminate misleading reviews are required to solve the problem. In this paper, a framework of a Machine Learning based fake review detection model has been proposed to identify which classification algorithm is the most effective with the proposed framework.