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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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