A Shallow Approach to Subjectivity Classification
conference paper
We present a shallow linguistic approach to subjectivity classification. Using multinomial kernel machines, we demonstrate that a data representation based on counting character n-grams is able to improve on results previously attained on the MPQA corpus using word-based n-grams and syntactic information. We compare two types of string-based representations: key substring groups and character n-grams. We find that word-spanning character n-grams significantly reduce the bias of a classifier, and boost its accuracy.1 Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
TNO Identifier
485625
Publisher
Association for the Advancement of Artificial Intelligence AAAI
Source title
2nd International Conference on Weblogs and Social Media, ICWSM 2008, 30 March - 2 April 2008, Seattle, WA, USA
Place of publication
Palo Alto, CA
Pages
216-217