This paper presents a bootstrapping process for learning linguistically rich extraction patterns to identify subjective expressions in natural language processing tasks. The process involves using high-precision classifiers to automatically label unannotated text, creating a large training set, and then applying an extraction pattern learning algorithm to generate patterns that capture the nuances of subjective language. These patterns are used to further identify subjective sentences, growing the training set and repeating the process. The experimental results show that this bootstrapping method increases recall while maintaining high precision, demonstrating the effectiveness of the learned patterns in distinguishing between factual and subjective information. The learned patterns also capture subtle connotations that individual words alone cannot express, enhancing the expressiveness of the system.This paper presents a bootstrapping process for learning linguistically rich extraction patterns to identify subjective expressions in natural language processing tasks. The process involves using high-precision classifiers to automatically label unannotated text, creating a large training set, and then applying an extraction pattern learning algorithm to generate patterns that capture the nuances of subjective language. These patterns are used to further identify subjective sentences, growing the training set and repeating the process. The experimental results show that this bootstrapping method increases recall while maintaining high precision, demonstrating the effectiveness of the learned patterns in distinguishing between factual and subjective information. The learned patterns also capture subtle connotations that individual words alone cannot express, enhancing the expressiveness of the system.