Hospital Recommendation using Sentimental Analysis

In the current digital era, most patients depend on online reviews to make well-informed choices when selecting a hospital for their treatment. These reviews frequently provide valuable insights regarding hospital services, patient care, and overall experiences. Nevertheless, the vast number of patient reviews complicates the manual analysis and extraction of meaningful conclusions. Sentiment analysis, a subset of Natural Language Processing (NLP), presents an effective solution to this challenge by automating the extraction of subjective information from text. This project, entitled “Hospital Recommendation using Sentimental Analysis”, seeks to create an automated system capable of analyzing patient reviews for hospitals and offering recommendations based on the sentiments expressed in those reviews. The system utilizes machine learning algorithms to categorize reviews into positive, negative, or neutral sentiments, thereby delivering valuable insights into public perception. By employing text mining techniques and sophisticated NLP methods, the system will scrutinize unstructured data from hospital reviews, empowering patients to make more informed decisions based on the perspectives of others. The foundation of the system comprises two main components: Sentiment Analysis and Recommendation System. The Sentiment Analysis model categorizes the review text into sentiment classifications, while the Recommendation System proposes hospitals that have garnered the most favorable reviews. Furthermore, the system will integrate patient preferences such as treatment type and location to enhance the personalization of hospital recommendations.