A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
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.
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.
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.
arxiv.org
Showing the best result for this search. See all results