AAAI Publications, Twenty-Second International FLAIRS Conference

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The Role of Knowledge-based Features in Polarity Classification at Sentence Level
Michael Wiegand, Dietrich Klakow

Last modified: 2009-03-17

Abstract


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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