Product Aspect Ranking and Its Applications
Abstract— Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both ﬁrms and users. However, the reviews are often disorganized, leading to difﬁculties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identiﬁes the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identiﬁed based on two observations: the important aspects are usually commented by a large number of consumers; and < Final Year Projects 2016 > 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. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the inﬂuence of consumer opinions given to each aspect over their overall opinions.
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