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.
Riloff and Wiebe REF present a bootstrapping approach to automatically create large training sets in order to learn extraction patterns for subjectivity.
Learning Extraction Patterns For Subjective Expressions
Conference On Empirical Methods In Natural Language Processing
Computer Science