Polarity Categorization on Product Reviews

Abstract

People search for other people’s opinions from the internet before purchasing a product, when they are not familiar about a specific product. With the help of reviews, ratings etc. online data presents useful information to customers for buying a product and for manufacturers to improve the quality of product. When an individual wants to make a decision about buying a product or using a service, they have access to a huge number of user reviews, but reading and analyzing all of them is a tedious task. Reading all of them is generally inefficient. There is a need for summarization in product reviews. Sentimental analysis helps customer visualize satisfaction while purchasing by simple summarization of these reviews into positive or negative two broader classified classes. The study aims to tackle the problem of sentiment polarity categorization. The data set is collected from amazon.com. The data set contains 376 instances of reviews of Nokia mobile in the form of a text file. Two classification algorithms namely Naïve Baye’s and Support Vector Machine Algorithms are taken to classify the reviews as positive, negative or neutral.