School of Management, Henan University of Technology, Zhengzhou, China.
Business School, Zhengzhou University, Zhengzhou, China.
Front Public Health. 2023 Jun 2;11:1088119. doi: 10.3389/fpubh.2023.1088119. eCollection 2023.
Throughout 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.
This 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.
Users 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.
This study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.
在整个 COVID-19 大流行期间，许多患者在在线医疗平台上寻求医疗建议。评论数据已成为支持用户选择医生的重要参考点。本研究以中国知名的在线咨询网站好大夫网（Haodf.com）为研究对象。
本研究从时间角度考察用户评论文本的主题和情感变化规律。我们还比较了 COVID-19 大流行前后用户评论文本的主题和情感变化特征。首先，我们使用 Python 从 2017 年到 2022 年从 Haodf.com 上抓取了 323,519 条关于 2,122 位医生的评论数据点。随后，我们采用潜在狄利克雷分配方法对主题进行聚类，使用 ROST 内容挖掘软件对用户情感进行分析。其次，根据困惑度计算的结果，我们将文本数据分为五个主题：诊断和治疗态度、医疗技能和道德、治疗效果、治疗方案和治疗过程。最后，我们确定了最重要的主题及其随时间的变化趋势。
用户主要关注诊断和治疗态度，其次是用户关注的医疗技能和道德。随着时间的推移，用户对诊断和治疗态度的关注度增加——尤其是在 2020 年 COVID-19 爆发期间，对诊断和治疗态度的关注度显著增加。用户对医疗技能和道德主题的关注度在 COVID-19 爆发期间开始下降，而治疗效果和方案的关注度则从 2017 年到 2022 年呈总体下降趋势。用户对治疗过程的关注度在 COVID-19 爆发前呈下降趋势，但在爆发后有所增加。关于情感分析，大多数用户对在线医疗服务表现出高度满意。然而，积极的用户情绪随时间呈下降趋势，尤其是在 COVID-19 爆发后。
本研究对协助用户选择治疗、医生决策和在线医疗平台设计具有参考价值。
