School of Information Technology, Zhejiang Financial College, Hangzhou, China.
Medical Record, Zhejiang University School of Medicine Sir Run Run Shaw Hospital, Hangzhou, China.
PLoS One. 2024 Feb 15;19(2):e0296855. doi: 10.1371/journal.pone.0296855. eCollection 2024.
This study aims to enhance governmental decision-making by leveraging advanced topic modeling algorithms to analyze public letters on the "People Call Me" online government inquiry platform in Zhejiang Province, China. Employing advanced web scraping techniques, we collected publicly available letter data from Hangzhou City between June 2022 and May 2023. Initial descriptive statistical analyses and text mining were conducted, followed by topic modeling using the BERTopic algorithm. Our findings indicate that public demands are chiefly focused on livelihood security and rights protection, and these demands exhibit a diversity of characteristics. Furthermore, the public's response to significant emergency events demonstrates both sensitivity and deep concern, underlining its pivotal role in government emergency management. This research not only provides a comprehensive landscape of public demands but also validates the efficacy of the BERTopic algorithm for extracting such demands, thereby offering valuable insights to bolster the government's agility and resilience in emergency responses, enhance public services, and modernize social governance.
本研究旨在通过利用先进的主题建模算法分析中国浙江省“民呼我为”在线政府咨询平台上的公众来信，来增强政府决策能力。我们采用先进的网页抓取技术，从杭州市 2022 年 6 月至 2023 年 5 月期间的公开信数据。我们进行了初步的描述性统计分析和文本挖掘，然后使用 BERTopic 算法进行主题建模。我们的研究结果表明，公众的需求主要集中在民生保障和权利保护方面，并且这些需求具有多样性的特征。此外，公众对重大突发事件的反应表现出敏感性和深切关注，突显了其在政府应急管理中的关键作用。这项研究不仅提供了公众需求的全面概况，还验证了 BERTopic 算法提取这些需求的有效性，从而为提高政府应对突发事件的敏捷性和弹性、改善公共服务和推进社会治理现代化提供了有价值的见解。
