Fake Customer Review Detection System

Online purchasing is rising bit by bit since each service or product is easily accessible. Sellers are obtaining more reaction to one’s corporation factors. Several people generally frustrated kinds of persons misdirect others by sharing false comments to encourage or damage the image of any specific goods or services according to wish. Such people are known as perception spammers and the false reviews they give are considered as fake comments. Although customer reviews could be beneficial, naïve confidence in such comments is unsafe for either the buyers or sellers. Many consumers read research before making any online purchase. Moreover, the comments could be misleading for additional benefit or profit, so any buying decision relied on web comments should be taken carefully. Our work is mainly directed to SA at the document level, more specifically, on movie reviews dataset. Machine learning techniques and SA methods are expected to have a major positive effect, especially for the detection processes of fake reviews in restaurant reviews, e-commerce, social commerce environments, and other domains. In machine learning-based techniques, algorithms such as SVM, NB, and NLP are applied for the classification purposes SVM is a type of learning algorithm that represents supervised machine learning approaches, and it is an excellent successful prediction approach. The SVM is also a robust classification approach. The main goal of our study is to classify restaurant reviews as a real review or fake review using SA algorithms with supervised learning techniques. Keywords: — Supervised Machine Learning Techniques, Support Vector Machine, Natural Language Processing and Naıve Bayes.