Abstract
This study examines the utilization of the K-Means algorithm for sentiment analysis in widely used hospital services utilizing the Python programming language. The main goal is to improve comprehension of patient satisfaction with the healthcare services provided at these hospitals. The data used for sentiment analysis was obtained via scraping patient evaluations from the web. The K-Means technique was utilized to classify the feelings into negative, neutral, and positive categories through the study of large-scale data. This investigation offers useful insights into the specific aspects that influence patients' opinions of healthcare services at their preferred hospitals. The study's findings provide valuable insights for hospital management to enhance the quality of healthcare services. Utilizing the K-Means algorithm in sentiment analysis facilitates the identification of prevalent trends and patterns that may not be discernible through manual techniques. Thus, this study integrates computational methodologies and sentiment analysis to offer a more holistic perspective on patient experiences at preferred hospitals.
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