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<Paper uid="E06-1027">
  <Title>Mining WordNet for Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Many of the tasks required for effective semantic tagging of phrases and texts rely on a list of words annotated with some lexical semantic features. Traditional approaches to the development of such lists are based on the implicit assumption of classical truth-conditional theories of meaning representation, which regard all members of a category as equal: no element is more of a member than any other (Edmonds, 1999). In this paper, we challenge the applicability of this assumption to the semantic category of sentiment, which consists of positive, negative and neutral subcategories, and present a dictionary-based Sentiment Tag Extraction Program (STEP) that we use to generate a fuzzy set of English sentiment-bearing words for the use in sentiment tagging systems 1.</Paragraph>
    <Paragraph position="1"> The proposed approach based on the fuzzy logic (Zadeh, 1987) is used here to assign fuzzy sentiment tags to all words in WordNet (Fellbaum, 1998), thatisitassigns sentiment tagsandadegree of centrality of the annotated words to the sentiment category. This assignment is based on Word-Net glosses. The implications of this approach for NLP and linguistic research are discussed.</Paragraph>
  </Section>
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