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This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjective words, an online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18 % absolute improvement in F-measure as compared to previously proposed semi-supervised methods. 1.
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Banea et al. (2008) studied this question.
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