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

Afiq Izzudin A Rahim1, Mohd Ismail Ibrahim1, Sook-Ling Chua2

  • 1Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a machine learning system to analyze Malaysian hospital Facebook reviews, classifying them by service quality dimensions and sentiment to enhance patient care. The system helps hospitals improve quality by understanding patient feedback effectively.

Keywords:
FacebookMalaysiaSERVQUALhealth informaticsmachine learningsentiment analysistopic classification

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Social media integration in healthcare is recognized but lacks systematic methods in Southeast Asia for quality improvement.
  • Hospitals need effective ways to utilize patient feedback from platforms like Facebook.

Purpose of the Study:

  • To develop a machine learning system for classifying Malaysian public hospital Facebook reviews.
  • To apply Service Quality (SERVQUAL) dimensions and sentiment analysis to patient feedback.
  • To enable automated analysis of patient experiences for hospital quality improvement.

Main Methods:

  • Developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model.
  • Created a Machine Learning Sentiment Analyzer (MLSA) using manually annotated reviews.
  • Trained and tested classifiers (Logistic Regression, Naive Bayes, SVM) using 5-fold cross-validation.

Main Results:

  • Average F1-scores for topic classification ranged from 0.687 to 0.757.
  • Support Vector Machine (SVM) consistently outperformed other methods in both SERVQUAL dimension and sentiment classification.
  • Demonstrated the effectiveness of supervised learning for automated analysis of patient feedback.

Conclusions:

  • Supervised learning can automatically identify SERVQUAL domains and sentiments from hospital Facebook reviews.
  • Malaysian healthcare providers can leverage this technology to gather and assess patient care data.
  • This approach offers a pathway to systematically improve hospital quality of care using social media insights.