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Autor:
Ma, Zheng; Nam, Jinseok; Weihe, Karsten:

Titel:
Improve sentiment analysis of citations with author modelling

Quelle:
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and Social media analysis (WASSA 2016) held in conjunction with NAACL 2016 Stroudsburg, PA : Association for Computational Linguistics (2016) , 122-127

URL des Volltextes:
http://www.aclweb.org/anthology/W16-0420

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Automatisierung, Autor, Bibliometrie, Modell, Text, Textanalyse, Zitat


Abstract(englisch):
In this paper, we introduce a novel approach to sentiment polarity classification of citations, which integrates data about the authors' reputation. More specifically, our method extends the h-index with citation polarities and utilizes it in sentiment classification of citation sentences. Our computational results show that our method yields significant improvement in terms of classification performance. (DIPF/Orig.)


DIPF-Abteilung:
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last modified Nov 11, 2016