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<Paper uid="P04-1035">
  <Title>A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as &amp;quot;thumbs up&amp;quot; or &amp;quot;thumbs down&amp;quot;. 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.</Paragraph>
  </Section>
class="xml-element"></Paper>