Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
Pang and Lee REF used a novel machine learning text categorization technique for the subjective classification of the document, integrating sentence-level subjectivity classification with document-level sentiment polarity.
A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based On Minimum Cuts
Annual Meeting Of The Association For Computational Linguistics
Computer Science