Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints
This work addresses text classification problems for consumer complaint analysis, though it appears incremental in combining existing techniques.
This study tackled the challenge of accurately capturing nuanced linguistic patterns in consumer complaint text classification by incorporating human-experience-trained algorithms and synthetic data generation methods. The result was enhanced classifier performance, reduced dataset acquisition costs, and improved evaluation metrics and robustness.
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.