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<Paper uid="P06-2079">
  <Title>Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews</Title>
  <Section position="8" start_page="617" end_page="617" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have examined two problems in document-level sentiment analysis, namely, review identification and polarity classification. We first found that review identification can be achieved with very high accuracies (97-99%) simply by training an SVM classifier using unigrams as features. We then examined the role of several linguistic knowledge sources in polarity classification. Our results suggested that bigrams and trigrams selected according to the weighted log-likelihood ratio as well as manually tagged term polarity information are very useful features for the task. On the other hand, no further performance gains are obtained by incorporating dependency-based information or filtering objective materials from the reviews using our proposed method. Nevertheless, the resulting polarity classifier compares favorably to state-of-the-art sentiment classification systems.</Paragraph>
  </Section>
class="xml-element"></Paper>