  This paper describes a weakly supervised system for sentiment analysis in the
movie review domain. The objective is to classify a movie review into a
polarity class, positive or negative, based on those sentences bearing opinion
on the movie alone. The irrelevant text, not directly related to the reviewer
opinion on the movie, is left out of analysis. Wikipedia incorporates the world
knowledge of movie-specific features in the system which is used to obtain an
extractive summary of the review, consisting of the reviewer's opinions about
the specific aspects of the movie. This filters out the concepts which are
irrelevant or objective with respect to the given movie. The proposed system,
WikiSent, does not require any labeled data for training. The only weak
supervision arises out of the usage of resources like WordNet, Part-of-Speech
Tagger and Sentiment Lexicons by virtue of their construction. WikiSent
achieves a considerable accuracy improvement over the baseline and has a better
or comparable accuracy to the existing semi-supervised and unsupervised systems
in the domain, on the same dataset. We also perform a general movie review
trend analysis using WikiSent to find the trend in movie-making and the public
acceptance in terms of movie genre, year of release and polarity.
