Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier


A. Rahim, Afiq Izzudin and Ibrahim, Mohd Ismail and Chua, Sook Ling and Musa, Kamarul Imran (2021) Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare, 9 (12). p. 1679. ISSN 2227-9032

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While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital’s Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.

Item Type: Article
Uncontrolled Keywords: Health informatics, machine learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 17 Jan 2022 09:50
Last Modified: 17 Jan 2022 09:50


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