The authors develop a means to detect nonmeritorious consumer complaints using natural language processing.

Machine learning (ML) has substantially advanced text classification by enabling automated understanding and categorization of complex unstructured textual data. However, accurately capturing nuanced linguistic patterns and contextual variations that are inherent in natural language, particularly within consumer complaints, remains a challenge. This study addresses these limitations by incorporating human-experience-trained algorithms that effectively recognize subtle semantic distinctions that are crucial for assessing consumer relief eligibility. Specifically, we propose synthetic data generation methods that leverage generative adversarial networks refined through expert labeling, complemented by featurization approaches for extracting textual representations. By combining expert-trained classifiers with high-quality synthetic data, this research demonstrates marked improvements in ML classifier performance, reduced dataset acquisition costs, and enhanced robustness across evaluation metrics in text classification tasks.