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Document Level Subjectivity Classification Experiments in DEFT'09 Challenge

Toprak, Cigdem and Gurevych, Iryna (2009):
Document Level Subjectivity Classification Experiments in DEFT'09 Challenge.
In: Proceedings of the DEFT'09 Text Mining Challenge, Paris, [Online-Edition: https://deft.limsi.fr/actes/2009/pdf/5_toprak.pdf],
[Conference or Workshop Item]

Abstract

In this paper, we present our supervised document level subjectivity classification experiments for English and French at the DEFT’09 Text Mining Challenge. We experiment with the word, POS, and lexicon-based features using 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 distributions favor different feature representations. Furthermore, on the English collection, we compare three, two of which are 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 relatively good results in English.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Toprak, Cigdem and Gurevych, Iryna
Title: Document Level Subjectivity Classification Experiments in DEFT'09 Challenge
Language: English
Abstract:

In this paper, we present our supervised document level subjectivity classification experiments for English and French at the DEFT’09 Text Mining Challenge. We experiment with the word, POS, and lexicon-based features using 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 distributions favor different feature representations. Furthermore, on the English collection, we compare three, two of which are 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 relatively good results in English.

Title of Book: Proceedings of the DEFT'09 Text Mining Challenge
Uncontrolled Keywords: Educational Natural Language Processing;UKP_a_ENLP;UKP_p_SENTAL;subjectivity analysis, document classification
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Event Location: Paris
Date Deposited: 31 Dec 2016 14:29
Official URL: https://deft.limsi.fr/actes/2009/pdf/5_toprak.pdf
Identification Number: TUD-CS-2009-0218
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