  In this paper, we present a novel approach to identify feature specific
expressions of opinion in product reviews with different features and mixed
emotions. The objective is realized by identifying a set of potential features
in the review and extracting opinion expressions about those features by
exploiting their associations. Capitalizing on the view that more closely
associated words come together to express an opinion about a certain feature,
dependency parsing is used to identify relations between the opinion
expressions. The system learns the set of significant relations to be used by
dependency parsing and a threshold parameter which allows us to merge closely
associated opinion expressions. The data requirement is minimal as this is a
one time learning of the domain independent parameters. The associations are
represented in the form of a graph which is partitioned to finally retrieve the
opinion expression describing the user specified feature. We show that the
system achieves a high accuracy across all domains and performs at par with
state-of-the-art systems despite its data limitations.
