REVIEW SPAM DETECTION USING MACHINE LEARNING

Abstract

Reviews, ratings, and experience stories left by customers on e-commerce websites and other online services are helpful to both buyers and sellers. The reviewer can foster more brand loyalty and aid in the understanding of other consumers' product experiences. Similar to how reviews help customers earn more profiles, reviews help businesses sell more things by enhancing customer satisfaction. However, suppliers may, unfortunately, abuse these review processes. For instance, one might fabricate positive evaluations to boost the reputation of a brand or attempt to denigrate rival brands' goods by posting fictitious reviews about them. Utilizing various machine learning techniques and tools are examples of existing solutions with supervision. Unlike previous work, I decided to use a wide range of vocabulary to work with, such as many datasets integrated into one enormous data set, rather than a confined dataset. Based on the reviews' text and emoji usage, sentiment analysis has been implemented. Review fraud is recognised and classed. The use of the Linear SVC, Support Vector Machine, and Random Forest algorithms yields the test results.