<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.</p>
</abstract>

As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.

Enhancing healthcare services recommendation through sentiment analysis

University Institute of Information Technology

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Enhancing healthcare services recommendation through sentiment analysis"}

As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of...

Enhancing healthcare services recommendation through sentiment analysis

Online veröffentlicht: 12. Dez. 2023

Seitenbereich: 330 - 344

Eingereicht: 31. Juli 2023

DOI: https://doi.org/10.2478/ausi-2023-0020

Schlüsselwörter
sentiment Analysis, opinion mining, entropy, feature extraction, quality of services

© 2023 Muhammad Rizwan Rashid Rana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Enhancing healthcare services recommendation through sentiment analysis

Online veröffentlicht: 12. Dez. 2023

Seitenbereich: 330 - 344

Eingereicht: 31. Juli 2023

DOI: https://doi.org/10.2478/ausi-2023-0020

Schlüsselwörtersentiment Analysis, opinion mining, entropy, feature extraction, quality of services

© 2023 Muhammad Rizwan Rashid Rana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
sentiment Analysis, opinion mining, entropy, feature extraction, quality of services