Agada, V. E.Ojo, A. K.2025-10-152024-05https://repository.ui.edu.ng/handle/123456789/11385This research introduces an innovative approach to improving sentiment analysis in healthcare service delivery by integrating Emotion and Affect Recognition (EAR) techniques into Emotional Variance Analysis (EVA). Leveraging logistic regression, the modifications, including adjusting confidence thresholds and utilizing the Rectified Linear Unit (ReLU) function, aim to address high polarity and enable real-time analysis. The methodology outlines a systematic process for EAR integration, offering practical insights for healthcare practitioners. In this study, additional datasets, including the Healthcare Patient Satisfaction Data Collection, the 9 Popular Patient Portal App Reviews for November 2023, and the HCAHPS Hospital Ratings Survey, are incorporated to enhance the robustness and reliability of the approach. The results across three healthcare centers demonstrate the effectiveness of this augmented approach, with comparisons against existing models using performance metrics. While showcasing promising potential, further research is needed to explore scalability and generalizability.enEmotional Variance Analysis (EVA)Healthcare Service DeliveryEmotion RecognitionAffect RecognitionLogistic RegressionFeature ScalingReal-Time AnalysisHealthcare ReviewsPatient SatisfactionData IntegrationText MiningArticle