Resumo
Patient feedback is a crucial component in identifying areas of improvement and enhancing service quality in healthcare. However, manual analysis of a large volume of reviews is challenging. This paper proposes a novel framework and software for sentiment analysis and topic modeling with the goal of automating this process, providing a more efficient method for data extraction and facilitating informed decision-making. The Google reviews dataset, a rich source of patient feedback, is used for this purpose. We present findings from related research to identify potential strategies for implementing this framework. The ultimate goal is to understand patient sentiments, identify common complaint topics, and highlight positive aspects of healthcare centers. This approach will provide valuable insights that are essential for the continuous improvement and success of healthcare centers.
Idioma original | Inglês |
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Título da publicação do anfitrião | DoCEIS |
Editores | Luis M. Camarinha-Matos, Filipa Ferrada |
Editora | Springer Science and Business Media Deutschland GmbH |
Páginas | 152-163 |
Número de páginas | 12 |
ISBN (impresso) | 9783031638503 |
DOIs | |
Estado da publicação | Publicadas - 1 jan. 2024 |
Evento | 15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024 - Caparica Duração: 3 jul. 2024 → 5 jul. 2024 |
Série de publicação
Nome | IFIP Advances in Information and Communication Technology |
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Volume | 716 IFIPAICT |
ISSN (impresso) | 1868-4238 |
ISSN (eletrónico) | 1868-422X |
Conferência
Conferência | 15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024 |
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País/Território | Portugal |
Cidade | Caparica |
Período | 3/07/24 → 5/07/24 |
Nota bibliográfica
Publisher Copyright:© IFIP International Federation for Information Processing 2024.