Product Aspect Ranking and Its Applications

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Name Product Aspect Ranking and Its Applications
Technology Dot net, MS SQL
Category Data Mining
Description 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: 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. 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. The experimental results on a review
corpus of 21 popular products in eight domains
demonstrate the effectiveness of the proposed
approach. Moreover, we apply product aspect ranking
to two real-world applications, i.e., document-level
sentiment classification and extractive review
summarization, and achieve significant performance
improvements, which demonstrate the capacity of
product aspect ranking in facilitating real-world
applications.
IEEE Paper Yes
IEEE Paper Year 2014

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