Development of decision-making technique based on sentiment analysis of crowdsourcing data in medical social media resources

Abstract

The object of the study is the decision-making modeling in the context of medical social media to increase the clinics’ effectiveness. The problem is to classify the patient reviews collected in the patient-clinic segment of the medical social media and to identify the situation related to the clinics’ activity by revealing the criteria characterizing the clinics’ activity out of the opinions. The proposed technique refers to lexicon-based sentiment analysis of opinions, the classification based on Valence Aware Dictionary and Sentiment Reasoner (VADER), the verification of the results accuracy with Multinomial Naive Bayes and Support Vector Machine, the manual sentiment analysis of opinions to detect criteria and the classification of opinions according to each criterion. Using this technique, out of 442587 patient reviews obtained from database cms_hospital_satisfaction_2020 of the Kaggle company generated on the basis of crowdsourcing of patient reviews on medical social media, 218914 patient reviews are classified as positive, 190360 – as neutral, and 33313 – as negative. The results accuracy is verified, and the clinics are rated by the «positive» opinions. 6 new criteria characterizing the clinics’ activity are discovered, and the identification of the situation related to the clinics’ activity based on the comparison of «positive» and «negative» opinions according to each criterion is presented. The possibility of using the results obtained from the identification to increase the clinics’ efficiency in making decisions is shown. The results obtained in this study can be used to improve the clinics’ performance according to public opinion. This opportunity involves the crowdsourcing of opinions about the clinic in the medical social media environment and the collection of opinions in a structured way.