A Shallow Approach to Subjectivity Classification

Authors

  • Stephan Raaijmakers TNO Information and Communication Technology
  • Wessel Kraaij TNO Information and Communication Technology

Abstract

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.

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Published

2021-09-25

How to Cite

Raaijmakers, S., & Kraaij, W. (2021). A Shallow Approach to Subjectivity Classification. Proceedings of the International AAAI Conference on Web and Social Media, 2(1), 216-217. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18658