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Research Article Open Access
Huge collections of consumer reviews are available on the Web expressing various opinions on multiple aspects of products .The important reviews are mostly not organized properly thereby creating problems in information navigation and knowledge acquisition. To address this problem, product aspect ranking framework is explored to automatically identify important product aspects or features from online consumer reviews.The important product aspects are identified based on two observations: i) the important aspects are usually commented on by a large number of consumers and ii) consumer opinions on the important aspects greatly influence their overall opinions on the product. The framework contains three main mechanisms, i.e., aspect sentiment classification, product aspect identification, and probabilistic aspect ranking algorithm. 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. The framework which aims at improving the usability of consumer reviews of a product. This paper provides the description of various techniques for product aspect identification and classification.
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Author(s): Ancy. J. S, Nisha. J.R