Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews
Introduction: The analysis of patients’ opinions is considered a valuable indicator for assessing the quality of healthcare services. The increasing volume of textual reviews about healthcare has made these reviews a critical factor in other patients’ decision-making processes when selecting medical...
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Shiraz University of Medical Sciences
2025-01-01
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| Series: | مجله علوم پزشکی صدرا |
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| Online Access: | https://smsj.sums.ac.ir/article_50773_7744ec54d51130074e8f1dca0bb3e230.pdf |
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| _version_ | 1849338282557571072 |
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| author | Faezeh Forootan Raouf Khayami Pirooz Shamsinejad |
| author_facet | Faezeh Forootan Raouf Khayami Pirooz Shamsinejad |
| author_sort | Faezeh Forootan |
| collection | DOAJ |
| description | Introduction: The analysis of patients’ opinions is considered a valuable indicator for assessing the quality of healthcare services. The increasing volume of textual reviews about healthcare has made these reviews a critical factor in other patients’ decision-making processes when selecting medical services. Consequently, researchers aimed to extract valuable insights, classify sentiments, and identify patient needs and behavioral patterns through sentiment analysis, thereby developing appropriate strategies to enhance patient satisfaction. However, patient reviews often contain a significant amount of specialized terminology, and existing sentiment analysis tools are typically trained on general-domain data. Therefore, to analyze these reviews accurately, it is essential to employ models and their combinations in a way that ensures reliable and valid results.Methods: To improve the efficiency and accuracy of sentiment analysis for Persian healthcare reviews, this study utilized the FastText-BERT hybrid embedding model for semantic relation extraction and the CNN-BiLSTM model for sentence-level sentiment classification.Results: The proposed framework achieved an accuracy of 86% and an F1-score of 84.99%.Conclusion: The results demonstrated that combining embedding models leverages the strengths of both approaches, enabling the identification of specialized and out-of-domain expressions and the extraction of semantic relationships between them. This combination significantly enhances the efficiency and accuracy of sentiment analysis. |
| format | Article |
| id | doaj-art-47c63a3aa36f48b9b35c48659ef0befc |
| institution | Kabale University |
| issn | 2322-4339 |
| language | fas |
| publishDate | 2025-01-01 |
| publisher | Shiraz University of Medical Sciences |
| record_format | Article |
| series | مجله علوم پزشکی صدرا |
| spelling | doaj-art-47c63a3aa36f48b9b35c48659ef0befc2025-08-20T03:44:28ZfasShiraz University of Medical Sciencesمجله علوم پزشکی صدرا2322-43392025-01-0113115516810.30476/smsj.2025.100979.146950773Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service ReviewsFaezeh Forootan0Raouf Khayami1Pirooz Shamsinejad2PhD Candidate, Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, IranAssociate Professor, Department of Computer Engineering, and Information Technology Shiraz University of Technology, Shiraz, IranAssistant Professor, Department of Computer Engineering, and Information Technology Shiraz University of Technology, Shiraz, IranIntroduction: The analysis of patients’ opinions is considered a valuable indicator for assessing the quality of healthcare services. The increasing volume of textual reviews about healthcare has made these reviews a critical factor in other patients’ decision-making processes when selecting medical services. Consequently, researchers aimed to extract valuable insights, classify sentiments, and identify patient needs and behavioral patterns through sentiment analysis, thereby developing appropriate strategies to enhance patient satisfaction. However, patient reviews often contain a significant amount of specialized terminology, and existing sentiment analysis tools are typically trained on general-domain data. Therefore, to analyze these reviews accurately, it is essential to employ models and their combinations in a way that ensures reliable and valid results.Methods: To improve the efficiency and accuracy of sentiment analysis for Persian healthcare reviews, this study utilized the FastText-BERT hybrid embedding model for semantic relation extraction and the CNN-BiLSTM model for sentence-level sentiment classification.Results: The proposed framework achieved an accuracy of 86% and an F1-score of 84.99%.Conclusion: The results demonstrated that combining embedding models leverages the strengths of both approaches, enabling the identification of specialized and out-of-domain expressions and the extraction of semantic relationships between them. This combination significantly enhances the efficiency and accuracy of sentiment analysis.https://smsj.sums.ac.ir/article_50773_7744ec54d51130074e8f1dca0bb3e230.pdfsentiment analysisdelivery of health caresymbiosissemanticpersianclinical coding |
| spellingShingle | Faezeh Forootan Raouf Khayami Pirooz Shamsinejad Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews مجله علوم پزشکی صدرا sentiment analysis delivery of health care symbiosis semantic persian clinical coding |
| title | Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews |
| title_full | Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews |
| title_fullStr | Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews |
| title_full_unstemmed | Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews |
| title_short | Using FastText-BERT to Extract Semantic Relations and Improve Sentiment Analysis of Persian Healthcare Service Reviews |
| title_sort | using fasttext bert to extract semantic relations and improve sentiment analysis of persian healthcare service reviews |
| topic | sentiment analysis delivery of health care symbiosis semantic persian clinical coding |
| url | https://smsj.sums.ac.ir/article_50773_7744ec54d51130074e8f1dca0bb3e230.pdf |
| work_keys_str_mv | AT faezehforootan usingfasttextberttoextractsemanticrelationsandimprovesentimentanalysisofpersianhealthcareservicereviews AT raoufkhayami usingfasttextberttoextractsemanticrelationsandimprovesentimentanalysisofpersianhealthcareservicereviews AT piroozshamsinejad usingfasttextberttoextractsemanticrelationsandimprovesentimentanalysisofpersianhealthcareservicereviews |