<?xml version="1.0" standalone="yes"?>
<Paper uid="W06-1642">
  <Title>Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis</Title>
  <Section position="4" start_page="355" end_page="356" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> Sentiment analysis has been extensively studied in recent years. The target of SA in this paper is wider than in previous work. For example, Yu and Hatzivassiloglou (2003) separated facts from opinions and assigned polarities only to opinions. In contrast, our system detects factual polar clauses as well as sentiments. null Unsupervised learning for sentiment analysis is also being studied. For example, Hatzivassiloglou and McKeown (1997) labeled adjectives as positive or negative, relying on semantic orientation. Turney (2002) used collocation with &amp;quot;excellent&amp;quot; or &amp;quot;poor&amp;quot; to obtain positive and negative clues for document classification. In this paper, we use contextual information which is wider than for the contexts they used, and address the problem of acquiring lexical entries from the noisy clues.</Paragraph>
    <Paragraph position="1"> Inter-sentential contexts as in our approach were used as a clue also for subjectivity analysis (Riloff and Wiebe, 2003; Pang and Lee, 2004), which is two-fold classification into subjective and objective sentences. Compared to it, this paper solves a more difficult problem: three-fold classification into positive, negative and non-polar expressions using imperfect coherency in terms of sentiment polarity.</Paragraph>
    <Section position="1" start_page="355" end_page="356" type="sub_section">
      <SectionTitle>
Learningmethodsforphrase-levelsentiment
</SectionTitle>
      <Paragraph position="0"> analysis closely share an objective of our approach. Popescu and Etzioni (2005) achieved high-precision opinion phrases extraction by using relaxation labeling. Their method iteratively assigns a polarity to a phrase, relying on semantic orientation of co-occurring words in specific relations in a sentence, but the scope of semantic orientation is limited to within a sentence. Wilson et al. (2005) proposed supervised learning, dividing the resources into  prior polarity and context polarity, which are similar to polar atoms and syntactic patterns in this paper, respectively. Wilson et al. prepared prior polarities from existing resources, and learned the context polarities by using prior polarities and annotated corpora. Therefore the prerequisite data and learned data are opposite from those in our approach. We took the approach used in this paper because we want to acquire more domain-dependent knowledge, and context polarity is easier to access in Japanese2. Our approach and their work can complement each other.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>