WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia
This addresses the problem of noisy sentiment classification for movie reviews by leveraging weak supervision, though it is incremental as it builds on existing resources and methods.
The paper tackles sentiment analysis in movie reviews by using Wikipedia to extract opinion-focused summaries, filtering out irrelevant text, and achieves improved accuracy over baseline methods without requiring labeled training data.
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.