Description

We have proposed a product aspect ranking framework to identify the important aspects of products from consumer reviews. The framework contains three main components, product aspect identification, aspect sentiment classification, and aspect ranking. First, we exploited the Pros and Cons reviews to improve aspect identification and sentiment classification on free-text reviews. We then developed a probabilistic aspect ranking algorithm to infer the importance of various aspects of a product from customer reviews. The experimental contains lot of consumer reviews of phone popular products in eight domains. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions on the product. In particular, given the consumer reviews of a product, we first identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classifier.