E-commerce is a transaction of buying or selling something online. E-commerce allows the customers to overcome the barriers of geographical and also allows them to purchase anytime and from anywhere and also consumers having the privilege to review positively or negatively on any product over the online. The consumer review is very important in knowing the product’s aspect and feature and it also very useful for both other consumers and firm. So in the way of finding the product aspect ranking we have proposed the methodologies in which it extracts the reviews and preprocessing, finding the aspect identification of the product, classifying the positive, negative and neutral reviews of the product by the sentiment classifier and also proposing the ranking algorithm used for the product ranking. In the data preprocessing there are methods available in which it initially differentiates the meaning and meaningless words and also it removes the postfix from each word and then tokenize each sentence by removing the emotion icons and also space. In aspect identification, we will identify the aspect from the numerous review which is given by the consumer whether it is positive or negative and on its basis of high or low score we will give a ranking. The main aims of sentiment classifier are to classify the review. The concurrent consideration of the aspect frequency and the pressure of consumer’s opinion given to each aspect on their overall opinions in the products aspects ranking and in its application.