Online Fake Review Detection Using SVM

Abstract

Abstract: After the pandemic, our overall life is changing and challenging that’s why our demand is changing now, we are focused on wellness, sustainability, technology, and the gig economy because of these trends we can observe the reflection in the changing desired need and limitation of seller and customer. Many customers can post a review on any website after making a purchase. Whether it’s an online purchase or an offline retail purchase. When customers buy a product online, they check the product reviews. This is very important for today’s e-commerce product decisions. There is a financial gain associated with writing fake reviews, which is why there has been a significant increase in misleading statements about certain product reviews on websites. Misleading reviews are dangerous reviews. Positive product reviews can attract customers and increase sales. Negative product reviews can reduce demand for that product and reduce sales. These misleading reviews are dangerous to your product’s reputation. In this paper, we use machine learning algorithms which are SVM Support vector machines, which are one of the most popular supervised learning algorithms used for both classification and regression problems. However, it is mainly used for machine-learning classification problems