Opinion Classification Based on Product Reviews from an Indian E-Commerce Website

Over the past decade, Indian e-commerce sector witnessed a huge growth. Currently this industry has approximately 40 million customers and it is expanding. These people express their experiences with various products, services in several websites, blogs, and social networking sites. To identify and extract any subjective knowledge from these huge unstructured user data, we need to develop a method that can collect, analyze, and classify user opinions. Two popular learning techniques (Supervised and Unsupervised) can be used to classify an opinion into two classes—“Positive” or “Negative.” In this paper, we propose an integrated framework for product review collection and unsupervised classification. The categorization of reviews is generated by the average semantic orientation of the phrases of suggestions or opinions in the review that holds adjectives as well as adverbs. A review can be categorized as an “Endorsed” one when the average semantic orientation is “Positive” otherwise it is an “Opposed” (“Negative”) one. Our proposed method has been tested on some real-life datasets collected from an Indian e-commerce website. The experimental results obtained show the efficiency of our proposed method for classification of product reviews.