基于微博评论文本挖掘分析公众对“医保报销”的态度和关注点 点击下载
| 论文标题： | 基于微博评论文本挖掘分析公众对“医保报销”的态度和关注点 |
| 英文标题： | |
| 中文摘要： | 摘 要 目的 基于微博平台评论文本，采用文本挖掘方法系统分析公众对“医保报销”的情感态度与关注重点，探索公众在现实制度执行中的真实体验与反馈。方法 以“医保报销”为关键词，采用Python网络爬虫技术抓取2023年12月至2025年4月的微博评论文本。运用词频分析、情感分析、语义网络分析及潜在狄利克雷分配（LDA）主题模型等方法，挖掘出评论文本的深层特征，探究公众对“医保报销”的关注点与情感态度。结果 共获得有效评论文本3 645条。公众对“医保报销”整体持弱积极态度，66.4%的评论为积极情感，32.4%为消极情感。语义网络分析显示，公众积极情感主要聚焦于医保改革成效及保障水平提升，而消极情感主要集中于报销规则、经济负担及医保政策执行等方面。LDA模型识别出8个核心主题，主要包括新药医保准入、中医药与生物医药的医保支持、特定医疗项目医保争议、异地就医医保结算、医保报销与就医负担以及创新药审批与医保支付。结论 公众对“医保报销”总体持肯定态度，但在执行层面仍有较多期待。建议在保障医保基金可持续性基础上，进一步提升报销流程的透明度与公平性，推动医保制度向科学化、规范化、高效化发展；同时，应重视社交媒体舆情反馈，强化政策沟通机制，增强公众对医保制度的获得感与信任度。 |
| 英文摘要： | OBJECTIVE Based on comment texts from the Weibo platform， this study adopts the text mining method to systematically analyze the public’s emotional attitudes and core concerns of “medical insurance reimbursement”， and explore the public’s real experiences and feedback in the implementation of the current system. METHODS With “medical insurance reimbursement” as the keyword， Python web crawler technology was used to collect Weibo comment texts from December 2023 to April 2025. Word frequency analysis， sentiment analysis， semantic network analysis and Latent Dirichlet Allocation （LDA） topic models were adopted to mine the in-depth features of comment texts and investigate the public’s concerns and emotional attitudes toward “medical insurance reimbursement”. RESULTS A total of 3 645 valid comment texts were obtained. The overall emotional of the public toward “medical insurance reimbursement” was weakly positive： 66.4% of the comments contained positive emotions， and 32.4% contained negative emotions. Semantic network analysis indicated that positive public emotions mainly centered on the achievements of medical insurance reform and the improvement of security level， while negative emotions mainly focused on reimbursement rules， economic burden and the implementation of medical insurance policies. The LDA model identified eight core themes， mainly including access of new drugs to medical insurance catalogs， medical insurance support for traditional Chinese medicine and biomedicine， disputes over medical insurance coverage of specific medical items， cross-regional medical treatment and medical insurance settlement， medical insurance reimbursement and medical burden， as well as the approval of innovative drugs and medical insurance payment. CONCLUSIONS The public generally holds a positive attitude toward “medical insurance reimbursement”， yet still has many expectations in terms of policy implementation. On the premise of guaranteeing the sustainability of medical insurance funds， it is suggested to further improve the transparency and fairness of the reimbursement process， and promote the scientific， standardized and efficient development of the medical insurance system. Meanwhile， attention should be paid to public opinion feedback on social media， and the policy communication mechanism should be strengthened， so as to enhance the public’s sense of gain and trust in the medical insurance system. |
| 期刊： | 2026年第37卷第13期 |
| 作者： | 刘巾式；欧阳萍萍；陈小霞；常浩娟；肖丽萍 |
| 英文作者： | LIU Jinshi，OUYANG Pingping，CHEN Xiaoxia，CHANG Haojuan，XIAO Liping |
| 关键字： | 医保报销; 文本挖掘; 社交平台; 自然语言处理; 情感分析; 语义网络分析; LDA主题模型 |
| KEYWORDS： | medical insurance reimbursement; text mining; social media platform; natural language processing; sentiment analysis; semantic network analysis; LDA topic model |
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| 文件大小： | 619.60Kb |
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