A sentiment-based comprehensive rating model with extensive dataset for nationwide hospital rating in Bangladesh using natural language processing and machine learning

Abstract

Bangladesh has a huge number of public and private hospitals in its districts. However, there is no trustworthy, unbiased resource for patients to choose the best hospitals concerning quality of service. The existing star-based rating systems are prone to manipulation and do not capture detailed feedback from the patients. This paper proposes an advanced model of hospital rating that grades the hospitals based on online reviews of the patients, considering aspects like patient experience and quality of care. This proposed model employs NLP and ML techniques to analyze the sentiment of patient feedback and extract insights from it. It is expected to provide a data-driven hospital rating system based solely on user experiences by integrating various dimensions of hospital service quality to identify strengths and areas for improvement across the country. A large dataset of structured and unstructured reviews is collected from online platforms. Text mining and advanced NLP techniques process sentiment data. Various machine learning models, such as SVM, BERT, and CNN, are trained and validated on pre-processed data for sentiment prediction. Steps to achieving this objective involve data collection, data preprocessing, sentiment analysis, and, eventually, aspect-based sentiment analysis using zero-shot aspect detection to generate hospital ratings based on 4 aspects: treatment quality, cleanliness, affordability, and service quality. Rating generation classify sentiments as positive, negative, or neutral, thereby dynamically, and in real time, rating a hospital. This model provides a more reliable and nuanced rating system that allows for transparent comparisons across hospitals and actionable insights into strengths and weaknesses. The framework is adaptable to other sectors, such as education and retail, providing an enlarged scope of application for sentiment analysis in service quality evaluation. For sentiment prediction of the reviews, BERT proves to be the best performing model out of all the models in terms of accuracy, precision, recall, and F1 score, producing 94.1%, 94.7%, 94.1%, and 94.4% respectively. For the aspect-based ranking of hospitals, the model is most confident in detecting treatment quality as it produces the highest teacher threshold of 0.51 for this aspect. It also produces the highest precision, F1 score, and agreement of 0.98, 0.97, and 0.96, respectively, for treatment quality, whereas, both affordability and service quality score the highest recall of 0.99.