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The Role of Knowledge-based Features in Polarity Classification at Sentence Level

  • Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy. In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.

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Author:Michael WiegandGND, Dietrich Klakow
Parent Title (English):Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference, 19–21 May 2009, Sanibel Island, Florida, USA
Publisher:AAAI Press
Place of publication:Menlo Park, CA
Editor:H. Chad Lane, Hans W. Guesgen
Document Type:Conference Proceeding
Year of first Publication:2009
Date of Publication (online):2019/01/23
GND Keyword:Computerlinguistik; Natürliche Sprache; Polarität; Text Mining
First Page:296
Last Page:301
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):Es gilt das UrhG