NLP-based Health Care- Hospital Recommendation Systems with Online Text Reviews by Patients Satisfaction

Recent times, these recommendations based on reviews play a vital role in the service industry. The hospital is assessing its quality of service using these surveys or studies posted in online forums. The ongoing pandemic also played a vital role in making the online review more popular. These statistical data and visualization are informative in representing the views of patient satisfaction towards health service. As the size of data is large and it is of varied size and format it is difficult to get consolidated results. The users share their emotions and feelings through this review. So, it is a challenge to assess the emotions of the patients. Sentiment analysis using machine learning makes our work easy in evaluating the scores visually. The reviews are analyzed using natural language processing (NLP), and the sentiment of the studies is analysed as positive, negative, and neutral using polarity ranking, which in turn is converted as the recommendation system based on patient reviews. This paper aims to propose a new method of recommending the hospital based on the sentiment of the previous user review. The thought of the user is collected from the various hospitals. The proposed (Healthcare Recommendation System) HRS system has nearly 0.5 mean absolute error, which states that the proposed HRS system is significantly effective.