  We present a statistical parsing framework for sentence-level sentiment
classification in this article. Unlike previous works that employ syntactic
parsing results for sentiment analysis, we develop a statistical parser to
directly analyze the sentiment structure of a sentence. We show that
complicated phenomena in sentiment analysis (e.g., negation, intensification,
and contrast) can be handled the same as simple and straightforward sentiment
expressions in a unified and probabilistic way. We formulate the sentiment
grammar upon Context-Free Grammars (CFGs), and provide a formal description of
the sentiment parsing framework. We develop the parsing model to obtain
possible sentiment parse trees for a sentence, from which the polarity model is
proposed to derive the sentiment strength and polarity, and the ranking model
is dedicated to selecting the best sentiment tree. We train the parser directly
from examples of sentences annotated only with sentiment polarity labels but
without any syntactic annotations or polarity annotations of constituents
within sentences. Therefore we can obtain training data easily. In particular,
we train a sentiment parser, s.parser, from a large amount of review sentences
with users' ratings as rough sentiment polarity labels. Extensive experiments
on existing benchmark datasets show significant improvements over baseline
sentiment classification approaches.
