Ensemble learning for improved sentiment analysis in doctor–patient communication

Abstract

Under stratified five-fold cross-validation, ensemble learning delivered the strongest and most balanced performance for three-class sentiment classification of clinician-patient dialogue, while transformers offered complementary precision on difficult cases. Attention- and feature-attribution analyses improved transparency, supporting clinical interpretability. Future work should scale to larger, multimodal (text/audio/vision) and multilingual datasets, and develop privacy-preserving, lightweight models for real-time deployment in clinical settings.

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