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<Paper uid="C04-1071">
  <Title>Deeper Sentiment Analysis Using Machine Translation Technology</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper has proposed a new approach to sentiment analysis: the translation from text to a set of semantic fragments. We have shown that the deep syntactic and semantic analysis makes possible the reliable extraction of sentiment units, and the outlining of sentiments became useful because of the aggregation of the variations in expressions, and the informative outputs of the arguments. The experimental results have shown that the precision of the sentiment polarity was much higher than for the conventional methods, and the sentiment units created by our system were less redundant and more informative than when using na&amp;quot;ive predicate-argument structures. Even though we exploited many advantages of deep analysis, we could create a sentiment analysis system at a very low development cost, becausemanyofthetechniquesformachinetranslation null can be reused naturally when we regard the extraction of sentiment units as a kind of translation.</Paragraph>
    <Paragraph position="1"> Many techniques which have been studied for the purpose of machine translation, such as word sense disambiguation (Dagan and Itai, 1994; Yarowsky, 1995), anaphora resolution (Mitamura et al., 2002), and automatic pattern extraction from corpora (Watanabe et al., 2003), can accelerate the further enhancement of sentiment analysis, or other NLP tasks. Therefore this work is the first step towards the integration of shallow and wide NLP, with deep NLP.</Paragraph>
  </Section>
class="xml-element"></Paper>