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Demystifying Hospital Experiences via Online Reviews Beyond Star Ratings: An Aspect-Based Mining of Online Reviews

In: Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1

Author

Listed:
  • Suman Agarwal

    (The Assam Royal Global University)

  • Ranjit Singh

    (Indian Institute of Information Technology Allahabad)

  • Bhartrihari Pandiya

    (National Forensic Sciences University)

  • Sahiba Khan

    (Indian Institute of Information Technology Allahabad)

Abstract

This study explores online reviews from patients and their families to understand customer perceptions in healthcare. Using data mining techniques, it analyses these reviews to gain insights into patient experiences across different hospital dimensions. By applying text-mining and part-of-speech tagging, the research systematically examines the aspects highlighted in the review data, such as doctors, staff, facilities, treatment, care, and overall management. Notably, Dharamshila Narayana Superspeciality Hospital in Delhi received a higher proportion of positive feedback using the DistilBERT model. There is also a positive correlation between star ratings and the quality of reviews, which in turn reflects the consumer experience. Additionally, the study compared the four machine learning models RF, DT, KNN, and Gradient Boosting for sentiment and bias prediction. Therefore, the government should have stringent policies to make customer reviews available to the public through complaint portals and keep a close monitor on hospitals with consistently low ratings.

Suggested Citation

  • Suman Agarwal & Ranjit Singh & Bhartrihari Pandiya & Sahiba Khan, 2025. "Demystifying Hospital Experiences via Online Reviews Beyond Star Ratings: An Aspect-Based Mining of Online Reviews," Springer Proceedings in Business and Economics, in: D P Goyal & Suprateek Sarker & Somnath Mukhopadhyay & Basav Roychoudhury & Parijat Upadhyay & Pradee (ed.), Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1, chapter 16, pages 311-336, Springer.
    • Handle: RePEc:spr:prbchp:978-981-96-2548-2_16
    DOI: 10.1007/978-981-96-2548-2_16
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