JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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SENTIMENT ANALYSIS OF PATIENT EXPERIENCE

Authors

  • Razan S. Alanazi
  • Hanan Ali Alshaher

Keywords:

sentiment analysis, natural language processing, deep learning, patient experience, machine learning, support vector machine

DOI:

https://doi.org/10.17654/0973514324020

Abstract

Healthcare sentiment analysis uses machine learning and natural language processing techniques to understand emotional tone, opinions, and attitudes around healthcare-related topics. This study examines patient attitudes on Twitter in Arabic, specifically regarding delays and late arrivals. The study uses machine learning, deep learning, ensemble learning, statistical analysis, and assessment methodologies to analyze tweets from January 1, 2020 to January 1, 2024 with Arabic keywords. We chose support vector machine (SVM) as a classical machine learning algorithm for supervised learning (SA) due to its efficacy and simplicity. The pre-training data set was used to investigate patient experience in Saudi Arabian hospitals. The results provide valuable insights into patient mood, allowing companies to identify areas for improvement and tailor their approach. The study improves sentiment analysis methodologies in the healthcare sector, allowing for more precise and effective analysis of patient experiences on social media sites.

Received: May 7, 2024
Accepted: May 16, 2024

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Published

2024-06-03

Issue

Vol. 24 No. 2 (2024)

Section

Articles

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How to Cite

SENTIMENT ANALYSIS OF PATIENT EXPERIENCE. (2024). JP Journal of Biostatistics, 24(2), 335-370. https://doi.org/10.17654/0973514324020

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