  Sentiment analysis on user reviews helps to keep track of user reactions
towards products, and make advices to users about what to buy. State-of-the-art
review-level sentiment classification techniques could give pretty good
precisions of above 90%. However, current phrase-level sentiment analysis
approaches might only give sentiment polarity labelling precisions of around
70%~80%, which is far from satisfaction and restricts its application in many
practical tasks. In this paper, we focus on the problem of phrase-level
sentiment polarity labelling and attempt to bridge the gap between phrase-level
and review-level sentiment analysis. We investigate the inconsistency between
the numerical star ratings and the sentiment orientation of textual user
reviews. Although they have long been treated as identical, which serves as a
basic assumption in previous work, we find that this assumption is not
necessarily true. We further propose to leverage the results of review-level
sentiment classification to boost the performance of phrase-level polarity
labelling using a novel constrained convex optimization framework. Besides, the
framework is capable of integrating various kinds of information sources and
heuristics, while giving the global optimal solution due to its convexity.
Experimental results on both English and Chinese reviews show that our
framework achieves high labelling precisions of up to 89%, which is a
significant improvement from current approaches.
