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%0 Conference Proceedings
%A Argamon, S.; Bloom, K.; Esuli, A. & Sebastiani, F.
%D 2007
%T Automatically Determining Attitude Type and Force for Sentiment Analysis
%E 
%B Proceedings of the 3rd Language and Technology Conference (LTC'07)
%C Poznan, Poland
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%6
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%P 369--373
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%F argamon:ada
%K analysis sentiment 
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%0 Conference Proceedings
%A Pang, Bo & Lee, Lillian
%D 2004
%T A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
%E 
%B ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
%C Morristown, NJ, USA
%I Association for Computational Linguistics
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%P 271
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%F 1218990
%K analysis sentiment 
%X 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.
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%U http://portal.acm.org/citation.cfm?id=1218990
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