A Shallow Approach to Subjectivity Classification

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

• Type: Conference
• Year: 2008
• Where: ICWSM
• Authors: Stephan Raaijmakers, Wessel Kraaij
• Keywords: Character N-grams, ICWSM 2008, Internet Technology, Word-based N-grams, Word-spanning Character N-grams