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Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives

  • In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labelled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are one option but they are expensive to obtain and do not necessarily work in an unknown domain. We show that extracting frequently occurring adjectives & adverbs of an unlabeled set of in-domain documents is an inexpensive alternative which works equally well throughout different domains.

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Author:Michael WiegandGND, Dietrich Klakow
Parent Title (English):Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009), May 14-16, 2009, Odense, Denmark
Series (Serial Number):NEALT Proceedings Series (4)
Publisher:Northern European Association for Language Technology
Place of publication:Uppsala
Editor:Kristiina Jokinen, Eckhard Bick
Document Type:Conference Proceeding
Year of first Publication:2009
Date of Publication (online):2019/01/30
GND Keyword:Computerlinguistik; Maschinelles Lernen; Natürliche Sprache; Polarität; Text Mining
First Page:198
Last Page:205
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):Es gilt das UrhG