Recommending and Ranking the Features Attracted by the Online Consumers Through Opinion Mining

The advancement in internet leads E-commerce organization to grow rapidly and allow people to purchase the products through online shopping. Before purchasing, the consumers gain knowledge about all the products available on the E-commerce websites. The online retailers use this opportunity to improve their business in the highly competitive e-commerce market. Thus they incorporated service recommendation systems to cope up with both the increasing number of customers as well as the competitive online trading system. Recommendation system can be offered by extracting opinions from the user through opinion mining. It is implemented in all organizations to evaluate their business as well as to improve the performance. The intention of this study is to analyze the various factors which attract the online consumers for purchasing a product in online. We explore the attitude of a consumer by extracting the ten features from online shopping website reviews and collected opinion poll from 480 consumers using opinion mining technique. Finally, by using feature-based top n recommendation algorithm the topmost features impressed the consumers are identified and visually represented in hierarchical order based on the opinionated score. The quantitative analysis ranks the features in order which its infer significance and it will help the small and medium enterprise to expand their business. Our evidence also suggests that high rated feature have more influence on the demand.