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<Paper uid="W03-1014">
  <Title>Learning Extraction Patterns for Subjective Expressions</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> This research explored several avenues for improving the state-of-the-art in subjectivity analysis. First, we demonstrated that high-precision subjectivity classification can be used to generate a large amount of labeled training data for subsequent learning algorithms to exploit. Second, we showed that an extraction pattern learning technique can learn subjective expressions that are linguistically richer than individual words or fixed phrases. We found that similar expressions may behave very differently, so that one expression may be strongly indicative of subjectivity but the other may not. Third, we augmented our original high-precision subjective classifier with these newly learned extraction patterns. This bootstrapping process resulted in substantially higher recall with a minimal loss in precision. In future work, we plan to experiment with different configurations of these classifiers, add new subjective language learners in the bootstrapping process, and address the problem of how to identify new objective sentences during bootstrapping.</Paragraph>
  </Section>
class="xml-element"></Paper>