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<Paper uid="P02-1053">
  <Title>Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews</Title>
  <Section position="11" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> This paper introduces a simple unsupervised learning algorithm for rating a review as thumbs up or down. The algorithm has three steps: (1) extract phrases containing adjectives or adverbs, (2) estimate the semantic orientation of each phrase, and (3) classify the review based on the average semantic orientation of the phrases. The core of the algorithm is the second step, which uses PMI-IR to calculate semantic orientation (Turney, 2001).</Paragraph>
    <Paragraph position="1"> In experiments with 410 reviews from Epinions, the algorithm attains an average accuracy of 74%. It appears that movie reviews are difficult to classify, because the whole is not necessarily the sum of the parts; thus the accuracy on movie reviews is about 66%. On the other hand, for banks and automobiles, it seems that the whole is the sum of the parts, and the accuracy is 80% to 84%.</Paragraph>
    <Paragraph position="2"> Travel reviews are an intermediate case.</Paragraph>
    <Paragraph position="3"> Previous work on determining the semantic orientation of adjectives has used a complex algorithm that does not readily extend beyond isolated adjectives to adverbs or longer phrases (Hatzivassiloglou and McKeown, 1997). The simplicity of PMI-IR may encourage further work with semantic orientation.</Paragraph>
    <Paragraph position="4"> The limitations of this work include the time required for queries and, for some applications, the level of accuracy that was achieved. The former difficulty will be eliminated by progress in hardware. The latter difficulty might be addressed by using semantic orientation combined with other features in a supervised classification algorithm.</Paragraph>
  </Section>
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