  The widespread use of online review sites over the past decade has motivated
businesses of all types to possess an expansive arsenal of user feedback to
mark their reputation. Though a significant proportion of purchasing decisions
are driven by average rating, detailed reviews are critical for activities like
buying expensive digital SLR camera. Since writing a detailed review for an
item is usually time-consuming, the number of reviews available in the Web is
far from many. Given a user and an item our goal is to identify the top-$k$
meaningful phrases/tags to help her review the item easily. We propose
general-constrained optimization framework based on three measures - relevance
(how well the result set of tags describes an item), coverage (how well the
result set of tags covers the different aspects of an item), and polarity (how
well sentiment is attached to the result set of tags). By adopting different
definitions of coverage, we identify two concrete problem instances that enable
a wide range of real-world scenarios. We develop practical algorithms with
theoretical bounds to solve these problems efficiently. We conduct experiments
on synthetic and real data crawled from the web to validate the effectiveness
of our solutions.
