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<Paper uid="E06-1025">
  <Title>Determining Term Subjectivity and Term Orientation for Opinion Mining</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses.</Paragraph>
    <Paragraph position="1"> To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of &amp;quot;subjective&amp;quot; terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation. This is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter.</Paragraph>
    <Paragraph position="2"> We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as &amp;quot;subjective&amp;quot; or &amp;quot;objective&amp;quot; is available, which is usually not the case.</Paragraph>
    <Paragraph position="3"> In this paper we confront the task of deciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of determining subjectivity and the problem of determining orientation. We tackle this problem by testing three different variants of a semi-supervised method previously proposed for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone.</Paragraph>
  </Section>
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