Patient-Centered Healthcare: A Framework for Analyzing Patient Feedback Through Sentiment Analysis and Topic Modeling

Patient feedback is a crucial component in identifying areas of improvement and enhancing service quality in healthcare. However, manual analysis of a large volume of reviews is challenging. This paper proposes a novel framework and software for sentiment analysis and topic modeling with the goal of automating this process, providing a more efficient method for data extraction and facilitating informed decision-making. The Google reviews dataset, a rich source of patient feedback, is used for this purpose. We present findings from related research to identify potential strategies for implementing this framework. The ultimate goal is to understand patient sentiments, identify common complaint topics, and highlight positive aspects of healthcare centers. This approach will provide valuable insights that are essential for the continuous improvement and success of healthcare centers.