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 originalInglês
Título da publicação do anfitriãoDoCEIS
EditoresLuis M. Camarinha-Matos, Filipa Ferrada
EditoraSpringer Science and Business Media Deutschland GmbH
Páginas152-163
Número de páginas12
ISBN (impresso)9783031638503
DOIs
Estado da publicaçãoPublicadas - 1 jan. 2024
Evento15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024 - Caparica
Duração: 3 jul. 20245 jul. 2024

Série de publicação

NomeIFIP Advances in Information and Communication Technology
Volume716 IFIPAICT
ISSN (impresso)1868-4238
ISSN (eletrónico)1868-422X

Conferência

Conferência15th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2024
País/TerritórioPortugal
CidadeCaparica
Período3/07/245/07/24

Nota bibliográfica

Publisher Copyright:
© IFIP International Federation for Information Processing 2024.

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