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- W2187874413 abstract "In this paper, we present our supervised document level subjectivity classification experiments for English andFrench at the DEFT’09 Text Mining Challenge. We experiment with the word, POS, and lexicon-based featuresusing an SVM classifier. Our word feature experiments (i) investigate the utility of the context information, and(ii) compare the binary and tf*idf feature representations in this task. We show that different class distributionsfavor different feature representations. Furthermore, on the English collection, we compare three, two of whichare well-known, opinon lexicons at this task: the subjectivity clues from (Wiebe and Riloff, 2005; Wilson et al.,2005), SentiWordNet (Esuli and Sebastiani, 2006), and a list of verbs compiled from (Santini, 2007; Biber et al.,1999)1 . We show that, despite its limited coverage, the verb lexicon, consisting of 156 verbs, establishes relativelygood results in English." @default.
- W2187874413 created "2016-06-24" @default.
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- W2187874413 date "2009-06-01" @default.
- W2187874413 modified "2023-09-26" @default.
- W2187874413 title "Document Level Subjectivity Classification Experiments in DEFT'09 Challenge" @default.
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