Learning extraction patterns for subjective expressions

  • Authors:

  • Ellen Riloff;Janyce Wiebe

  • Affiliations:

  • University of Utah, Salt Lake City, UT;University of Pittsburgh, Pittsburgh, PA

  • Venue:
  • EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
  • Year:
  • 2003

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Abstract

This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.