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<Paper uid="E06-1025">
  <Title>Determining Term Subjectivity and Term Orientation for Opinion Mining</Title>
  <Section position="8" start_page="198" end_page="199" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented a method for determining both term subjectivity and term orientation for opinion mining applications. This is a valuable advance with respect to the state of the art, since past work in this area had mostly confined to determining term orientation alone, a task that (as we have ar- null fied as Objective, for each learner and for each choice of the TrKo set.</Paragraph>
    <Paragraph position="1">  gued) has limited practical significance in itself, given the generalized absence of lexical resources that tag terms as being either Subjective or Objective. Our algorithms have tagged by orientation and subjectivity the entire General Inquirer lexicon, a complete general-purpose lexicon that is the de facto standard benchmark for researchers in this field. Our results thus constitute, for this task, the first baseline for other researchers to improve upon.</Paragraph>
    <Paragraph position="2"> Unfortunately, our results have shown that an algorithm that had shown excellent, state-of-the-art performance in deciding term orientation (Esuli and Sebastiani, 2005), once modified for the purposes of deciding term subjectivity, performs more poorly. This has been shown by testing several variants of the basic algorithm, some of them involving radically different supervised learning policies. The results suggest that deciding term subjectivity is a substantially harder task that deciding term orientation alone.</Paragraph>
  </Section>
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