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    <title>A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts</title>
    <description>A sentimental education</description><link>http://www.bibsonomy.org/bibtex/20b6f267021dde9c3181e88c5100a7552/renew</link>
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    <dc:date>2008-02-22T20:53:15+01:00</dc:date>
    <dc:subject>analysis sentiment </dc:subject>
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  <a href="http://www.bibsonomy.org/bibtex/20b6f267021dde9c3181e88c5100a7552/renew">A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts</a>
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  <span style="color:#555555;"> 
    Bo <a href="http://www.bibsonomy.org/author/Pang">Pang</a>         	     	 
        	  and Lillian <a href="http://www.bibsonomy.org/author/Lee">Lee</a>         	     	 
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  <em>ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics</em>
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  (2004)
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        <swrc:address>Morristown, NJ, USA</swrc:address><swrc:booktitle>ACL &#039;04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics</swrc:booktitle><swrc:pages>271</swrc:pages><swrc:publisher><swrc:Organization swrc:name="Association for Computational Linguistics"/></swrc:publisher><swrc:title>A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts</swrc:title><swrc:year>2004</swrc:year><swrc:keywords>analysis sentiment </swrc:keywords><swrc:date>2008-02-22 20:53:15.0</swrc:date><swrc:abstract>Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as &#034;thumbs up&#034; or &#034;thumbs down&#034;. 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.</swrc:abstract><swrc:hasExtraField>
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