Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints

TL;DR

This paper evaluates various metrics for NLP-based assessment and text generation of consumer complaints, emphasizing the importance of nuanced linguistic understanding and synthetic data integration to improve classifier performance.

Contribution

It introduces a novel approach combining human-experience-trained algorithms with synthetic data generation to enhance NLP evaluation metrics for consumer complaints.

Findings

Improved classifier accuracy with expert-trained models

Synthetic data reduces dataset costs

Enhanced robustness of NLP assessments

Abstract

Machine learning (ML) has significantly advanced text classification by enabling automated understanding and categorization of complex, unstructured textual data. However, accurately capturing nuanced linguistic patterns and contextual variations inherent in natural language, particularly within consumer complaints, remains a challenge. This study addresses these issues by incorporating human-experience-trained algorithms that effectively recognize subtle semantic differences crucial for assessing consumer relief eligibility. Furthermore, we propose integrating synthetic data generation methods that utilize expert evaluations of generative adversarial networks and are refined through expert annotations. By combining expert-trained classifiers with high-quality synthetic data, our research seeks to significantly enhance machine learning classifier performance, reduce dataset acquisition costs, and improve overall evaluation metrics and robustness in text classification tasks.