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Abstract: As we know, now a day’s online shopping become a daily activity for humans. Before going to buy any product in e-commerce business organizations. Reviews are the one of the important ways to check reliability of a product. Customer will check reviews posted by other customers to buy a product. If a customer bought a product by seeing fake review, if the product is really good no problem otherwise a product loses its reliability. We are here to perform sentiment analysis on restaurant reviews to find number of correct and number of wrong predictions made by the classifier which is further helpful to classify reviews into real or fake.The classifiers used in our project are Natural Language Processing, Support vector Machine (SVM), and Naıve Bayes. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches the highest accuracy not only in text classification, but also in detecting fake reviews.