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<Paper uid="P04-3025">
  <Title>Incorporating topic information into sentiment analysis models</Title>
  <Section position="4" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Motivation
</SectionTitle>
    <Paragraph position="0"> In the past, work has been done in the area of characterizing words and phrases according to their emotive tone (Turney and Littman, 2003; Turney, 2002; Kamps et al., 2002; Hatzivassiloglou and Wiebe, 2000; Hatzivassiloglou and McKeown, 2002; Wiebe, 2000), but in many domains of text, the values of individual phrases may bear little relation to the overall sentiment expressed by the text. Pang et al. (2002)'s treatment of the task as analogous to topic-classification underscores the difference between the two tasks. A number of rhetorical devices, such as the drawing of contrasts between the reviewed entity and other entities or expectations, sarcasm, understatement, and digressions, all of which are used in abundance in many discourse domains, create challenges for these approaches. It is hoped that incorporating topic information along the lines suggested in this paper will be a step towards solving some of these problems.</Paragraph>
  </Section>
class="xml-element"></Paper>