Clear View: An Opinion Mining Approach to Combat Fake Reviews

It is crucial to identify and remove fake reviews from the provided dataset using various Natural Language Processing (NLP) approaches for a number of reasons. In order to predict the accuracy of how genuine the reviews in a given dataset are, two distinct Machine Learning (ML) models are applied to train the false review dataset in this research. When relying on product reviews for the item found online across various websites and applications, the prevalence of fraudulent reviews is rising in the e-commerce sector and even on other platforms. Before making a purchase, the company's items were trusted. Therefore, it is necessary to address the issue of phony reviews so that major e-commerce companies like Flipkart, Amazon, and others can resolve it and get rid of spammers and bogus reviewers. To keep consumers from losing faith in e-commerce sites. Websites and applications with a few thousand users can use this model to forecast the legitimacy of reviews, allowing the proprietors of those websites to take appropriate action. Random forest and Naive Bayes techniques were used to construct this model. By using these models, one may quickly determine how many spam reviews there are on a website or application.