A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

dc.creatorPang, Bo
dc.creatorLee, Lillian
dc.date2004-09-29
dc.date.accessioned2026-07-07T03:21:49Z
dc.date.available2026-07-07T03:21:49Z
dc.descriptionSentiment 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.
dc.descriptionData available at http://www.cs.cornell.edu/people/pabo/movie-review-data/
dc.identifierhttps://arxiv.org/abs/cs/0409058
dc.identifierhttp://arxiv.org/abs/cs/0409058
dc.identifierProceedings of the 42nd ACL, pp. 271--278, 2004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32351
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleA Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
dc.typetext

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