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Background: Patient feedback has emerged as a critical measure of healthcare quality and a key driver of organizational performance. Traditional manual analysis of unstructured patient feedback presents significant challenges as data volumes grow, making it difficult to extract meaningful patterns and actionable insights efficiently. Objective: To develop and evaluate a comprehensive methodology for analyzing patient feedback data using natural language processing (NLP) and Knowledge Discovery in Databases (KDD) approaches, aiming to identify key patterns, themes, and variations in patient experience across different demographic groups and care settings, and to translate these insights into actionable improvements in healthcare delivery. Methods: This study applied an integrated KDD-Action Research framework to analyze 126,134 patient feedback entries collected at Alexandra Hospital, Singapore in 2023. A comprehensive suite of text mining techniques including sentiment analysis, topic modeling, emotion detection, and aspect-based sentiment analysis was employed to uncover patterns in patient-reported experiences. The dataset included 92,578 (73.4%) entries containing free-text comments, comprising 1,568,932 tokens with an average comment length of 16.9 words. Multiple analytical techniques were triangulated to ensure validity and reliability of findings. Stakeholder engagement throughout the research process facilitated the translation of analytical insights into practical improvements. Ethics approval was obtained from the National Healthcare Group Domain Specific Review Board with a waiver of informed consent granted for this retrospective analysis of de-identified patient feedback data. Results: Text mining analysis revealed a moderately positive overall sentiment across the feedback corpus (average polarity score: 0.42), with 68.8% of comments classified as positive, 25.4% neutral, and 5.8% negative. Topic modeling identified 10 distinct topics including staff attitude and service (10.2%), healthcare staff professionalism (10.1%), hospital environment (10.0%), and waiting time (10.0%). Aspect-based sentiment analysis highlighted nurse attitude (sentiment score: 0.65), staff helpfulness (0.61), and doctor expertise (0.58) as the most positive aspects, while waiting time (-0.42) and billing transparency (-0.28) emerged as the most negative. Demographic segmentation revealed significant variations in patient priorities, with younger patients (

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