A Comparative Study of Machine Learning Methods for Automated Customer Service Dialogue Quality Assessment

The rapid expansion of digital customer service channels has created an urgent need for automated quality assessment methods capable of evaluating dialogue interactions at scale. This paper presents a comprehensive comparative study of machine learning approaches for automated assessment of customer service dialogue quality, examining traditional machine learning algorithms, deep learning architectures, and transformer-based models. A multi-dimensional quality assessment framework is proposed, incorporating three primary evaluation categories: information accuracy, communication appropriateness, and process compliance. An experimental evaluation on a customer service dialogue dataset demonstrates that BERT-based models achieve superior overall classification accuracy (94.2%), while traditional methods offer advantages in computational efficiency and interpretability. The analysis reveals significant performance differences across service defect categories, with transformer models excelling at detecting subtle compliance violations and attitude-related issues. These findings provide practical guidance for enterprises seeking to implement standardized quality-monitoring systems that align with consumer protection regulations.