Critical product features' identification using an opinion analyzer

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.