Abstract
Hospitals use surveys to assess the satisfaction of the patient and improve their services. Similarly, the recommender systems use reviews to improve the quality of hospital suggestions to customers. In representing patient satisfaction information, statistical data and visualization are useful. But it's very hard due to the size and dimension of the dataset. People share their subjective ideas and feelings. This work provides an unattended information methodology that finds from the patient's point of perspective the particular problems reflected by the dataset. Natural language processing is used to analyze the reviews. Analysis of sentiment is used to rank and convert to recommendations that patients can use. Therefore, people will receive appropriate hospital information.
Published Version
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