Doctor selection based on aspect-based sentiment analysis and neutrosophic TOPSIS method

Physician-rating sites have become a convenient platform for patients to choose doctors. However, selecting an appropriate doctor through numerous online reviews is challengeable for patients. Although studies show that patients have different preferences for the aspects of healthcare services, existing doctor ranking methods rarely consider such preference information. Besides, they seldom handle the neutral sentiment information in patient reviews. To better assist patients in doctor selection, we propose a novel decision-making method that combines aspect-based sentiment analysis, single-valued neutrosophic sets and an extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The method utilizes Word2Vec to construct a feature dictionary of patient satisfaction. A rule-based approach is employed to extract the aspects and the related sentiments from patients’ text reviews. Moreover, it takes advantage of single-valued neutrosophic sets to address the positive, neutral and negative sentiment information. On this basis, we rank the doctors with an extended TOPSIS method considering the patient’s attitudinal character and the preference information. A case study on a review dataset demonstrates the stability and flexibility of the method. The comparative analysis reveals that our method can efficiently reduce information loss and it is more practical than existing methods. Considering patients’ different preferences for the aspects of healthcare services, the method can better provide healthcare decision support for patients.