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

Suman Agarwal (), Ranjit Singh (), Bhartrihari Pandiya () and Sahiba Khan ()
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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

Chapter 16 in Leveraging Emerging Technologies and Analytics for Empowering Humanity, Vol. 1, 2025, pp 311-336 from Springer

Abstract: 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.

Keywords: Customer experience; Star-rating; Data mining; Customer reviews (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-96-2548-2_16

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DOI: 10.1007/978-981-96-2548-2_16

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