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<Paper uid="P05-1015">
  <Title>respect to rating scales</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We address the rating-inference problem, wherein rather than simply decide whether a review is thumbs up or thumbs down , as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to ve stars ). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, three stars is intuitively closer to four stars than to one star .</Paragraph>
    <Paragraph position="1"> We rst evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labeling formulation of the problem, that alters a given a6 -ary classi er's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide signi cant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.</Paragraph>
  </Section>
class="xml-element"></Paper>