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<Paper uid="P97-1023">
  <Title>Predicting the Semantic Orientation of Adjectives</Title>
  <Section position="3" start_page="0" end_page="174" type="intro">
    <SectionTitle>
2 Overview of Our Approach
</SectionTitle>
    <Paragraph position="0"> Our approach relies on an analysis of textual corpora that correlates linguistic features, or indicators, with 1 Exceptions include a small number of terms that are both negative from a pragmatic viewpoint and yet stand in all antonymic relationship; such terms frequently lexicalize two unwanted extremes, e.g., verbose-terse.</Paragraph>
    <Paragraph position="1"> 2 Except implicitly, in the form of definitions and usage examples.</Paragraph>
    <Paragraph position="2">  semantic orientation. While no direct indicators of positive or negative semantic orientation have been proposed 3, we demonstrate that conjunctions between adjectives provide indirect information about orientation. For most connectives, the conjoined adjectives usually are of the same orientation: compare fair and legitimate and corrupt and brutal which actually occur in our corpus, with ~fair and brutal and *corrupt and legitimate (or the other cross-products of the above conjunctions) which are semantically anomalous. The situation is reversed for but, which usually connects two adjectives of different orientations. null The system identifies and uses this indirect information in the following stages:  1. All conjunctions of adjectives are extracted from the corpus along with relevant morphological relations.</Paragraph>
    <Paragraph position="3"> 2. A log-linear regression model combines informa null tion from different conjunctions to determine if each two conjoined adjectives are of same or different orientation. The result is a graph with hypothesized same- or different-orientation links between adjectives.</Paragraph>
    <Paragraph position="4"> 3. A clustering algorithm separates the adjectives into two subsets of different orientation. It places as many words of same orientation as possible into the same subset.</Paragraph>
    <Paragraph position="5"> 4. The average frequencies in each group are compared and the group with the higher frequency is labeled as positive.</Paragraph>
    <Paragraph position="6"> In the following sections, we first present the set of adjectives used for training and evaluation. We next validate our hypothesis that conjunctions constrain the orientation of conjoined adjectives and then describe the remaining three steps of the algorithm. After presenting our results and evaluation, we discuss simulation experiments that show how our method performs under different conditions of sparseness of data.</Paragraph>
  </Section>
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