<?xml version="1.0" standalone="yes"?>
<Paper uid="P04-1034">
  <Title>The Sentimental Factor: Improving Review Classification via Human-Provided Information</Title>
  <Section position="6" start_page="2" end_page="2" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> In business settings, there is growing interest in learning product reputations from the Internet. For such problems, it is often difficult or expensive to obtain labeled data. As a result, a change in modeling strategies is needed, towards approaches that require less supervision. In this paper we provide a framework for allowing human-provided information to be combined with unlabeled documents and labeled documents. We have found that this framework enables improvements over existing techniques, both in terms of the speed of model estimation and in classification accuracy. As a result, we believe that this is a promising new approach to problems of practical importance.</Paragraph>
  </Section>
class="xml-element"></Paper>