This is like testing two kinds of "emotion detectors" for customer messages: older rule/ML-based detectors versus newer deep learning/AI models, to see which better understands if customers are happy, angry, or neutral.
Organizations struggle to accurately gauge customer satisfaction and intent from large volumes of text (support tickets, chats, emails, reviews). This study compares machine learning and deep learning sentiment methods to identify which approaches are more reliable and scalable for real-world sentiment monitoring and customer experience analytics.
The moat is not the algorithms themselves (commodity), but how a company can combine these findings with its proprietary historical customer conversations, labeled sentiment data, and domain-specific lexicons to build more accurate, domain-tuned sentiment systems embedded in its customer-service workflows.
Classical-ML (Scikit/XGBoost)
Unknown
High (Custom Models/Infra)
Training/inference cost and latency for deep learning models at scale, plus labeled data requirements for each language/domain.
Early Majority
This work focuses specifically on a head-to-head comparison of traditional machine learning versus deep learning sentiment methods, which helps practitioners choose an appropriate complexity level and cost/performance trade-off for customer-service sentiment analysis instead of defaulting blindly to deep learning or LLMs.