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This paper describes a weakly supervised system for sentiment analysis in themovie review domain. The objective is to classify a movie review into apolarity class, positive or negative, based on those sentences bearing opinionon the movie alone. The irrelevant text, not directly related to the revieweropinion on the movie, is left out of analysis. Wikipedia incorporates the worldknowledge of movie-specific features in the system which is used to obtain anextractive summary of the review, consisting of the reviewer's opinions aboutthe specific aspects of the movie. This filters out the concepts which areirrelevant or objective with respect to the given movie. The proposed system,WikiSent, does not require any labeled data for training. The only weaksupervision arises out of the usage of resources like WordNet, Part-of-SpeechTagger and Sentiment Lexicons by virtue of their construction. WikiSentachieves a considerable accuracy improvement over the baseline and has a betteror comparable accuracy to the existing semi-supervised and unsupervised systemsin the domain, on the same dataset. We also perform a general movie reviewtrend analysis using WikiSent to find the trend in movie-making and the publicacceptance in terms of movie genre, year of release and polarity.

WikiSent: Weakly Supervised Sentiment Analysis through Extractive Summarization with Wikipedia