Leveraging Support Vector Machine for Accurate Sentiment Analysis in Healthcare

Sentiment AI leverages artificial intelligence to analyze human emotions across various fields. This research focuses on understanding patient experiences with healthcare facilities by using a Support Vector Machine (SVM) algorithm in machine learning. The primary goal is to train a model to analyze patient reviews, identifying key features of each facility, such as cleanliness, doctor accessibility, and patient treatment. Implemented in Python with necessary machine learning libraries, the model can extract insights from patient responses and assess their sentiments. This study aims to assist patients in selecting suitable healthcare facilities by calculating a “goodness score” for each one, representing advancements in AI and machine learning in healthcare