Toprak, Cigdem ; Gurevych, Iryna (2009)
Document Level Subjectivity Classification Experiments in DEFT'09 Challenge.
Paris
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Toprak, Cigdem ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Document Level Subjectivity Classification Experiments in DEFT'09 Challenge |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2009 |
| Buchtitel: | Proceedings of the DEFT'09 Text Mining Challenge |
| Veranstaltungsort: | Paris |
| URL / URN: | https://deft.limsi.fr/actes/2009/pdf/5_toprak.pdf |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Educational Natural Language Processing;UKP_a_ENLP;UKP_p_SENTAL;subjectivity analysis, document classification |
| ID-Nummer: | TUD-CS-2009-0218 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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