<?xml version="1.0" standalone="yes"?>
<Paper uid="H05-2017">
  <Title>OPINE: Extracting Product Features and Opinions from Reviews</Title>
  <Section position="4" start_page="32" end_page="32" type="relat">
    <SectionTitle>
3 Related Work
</SectionTitle>
    <Paragraph position="0"> The previous review-mining systems most relevant to our work are (Hu and Liu, 2004) and (Kobayashi et al., 2004). The former's precision on the explicit feature extraction task is 22% lower than OPINE's while the latter employs an iterative semi-automatic approach which requires significant human input; neither handles implicit features. Unlike previous research on identifying the subjective character and the polarity of phrases and sentences ((Hatzivassiloglou and Wiebe, 2000; Turney, 2003) and many others), OPINE identifies the context-sensitive polarity of opinion phrases. In contrast to supervised methods which distinguish among strength levels for sentences or clauses ((Wilson et al., 2004) and others), OPINEuses an unsupervised constraint-based opinion ranking approach.</Paragraph>
  </Section>
class="xml-element"></Paper>