Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example
IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation web...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088119/full |
| _version_ | 1797813713055514624 |
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| author | Chaoyang Li Shengyu Li Jianfeng Yang Jingmei Wang Yiqing Lv |
| author_facet | Chaoyang Li Shengyu Li Jianfeng Yang Jingmei Wang Yiqing Lv |
| author_sort | Chaoyang Li |
| collection | DOAJ |
| description | IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design. |
| first_indexed | 2024-03-13T07:55:52Z |
| format | Article |
| id | doaj.art-23464feccea2491cb9742a1848b6017e |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-13T07:55:52Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-23464feccea2491cb9742a1848b6017e2023-06-02T04:27:17ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-06-011110.3389/fpubh.2023.10881191088119Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an exampleChaoyang Li0Shengyu Li1Jianfeng Yang2Jingmei Wang3Yiqing Lv4School of Management, Henan University of Technology, Zhengzhou, ChinaSchool of Management, Henan University of Technology, Zhengzhou, ChinaBusiness School, Zhengzhou University, Zhengzhou, ChinaSchool of Management, Henan University of Technology, Zhengzhou, ChinaSchool of Management, Henan University of Technology, Zhengzhou, ChinaIntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088119/fullonline medical platformHaodf websitetext miningtopic analysissentiment analysisCOVID-19 pandemics |
| spellingShingle | Chaoyang Li Shengyu Li Jianfeng Yang Jingmei Wang Yiqing Lv Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example Frontiers in Public Health online medical platform Haodf website text mining topic analysis sentiment analysis COVID-19 pandemics |
| title | Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example |
| title_full | Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example |
| title_fullStr | Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example |
| title_full_unstemmed | Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example |
| title_short | Topic evolution and sentiment comparison of user reviews on an online medical platform in response to COVID-19: taking review data of Haodf.com as an example |
| title_sort | topic evolution and sentiment comparison of user reviews on an online medical platform in response to covid 19 taking review data of haodf com as an example |
| topic | online medical platform Haodf website text mining topic analysis sentiment analysis COVID-19 pandemics |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1088119/full |
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