E-commerce is a widely increasing business in the world with increasing revenues every year by manifold times. However, multiple e-commerce websites have same products and it becomes difficult for the user to select the product from the best e-commerce website as the reviews for each product are more in number. It is difficult for the user to go through these reviews as they are in large number and its time consuming. The machine learning tools and techniques such as classification have now made it easy and simple to process text datasets and find insights from it. In this paper, a simple text mining and processing approach is illustrated, and the sample dataset of product reviews are used from Amazon. For the classification model, a probabilistic based classifier model called Naïve Bayes is used, which will classify the user reviews in three categories, such as positive, neutral, and negative reviews. This will help to analyse the sentiments of the users who have already purchased the product and will help the customer. The final output will consist of overall review and rating of the product which will help the user to get a clear knowledge about the product and whether to make a purchase call or not. By applying the Naïve Bayes technique, the user reviews are classified successfully giving an accuracy of 72.46%.