Results of the analysis of affective tendencies in letters.

Results of the analysis of affective tendencies in letters.</p

Secondary topic keyword contribution line chart.

The chart depicts the trend of declining keyword contribution within various secondary topics. Each line in the chart represents a secondary topic, and the trajectory of the line illustrates the change in keyword contribution (c-TF-IDF score) with respect to the keyword ranking (Term Rank). The horizontal axis, "Term Rank," indicates the ranking of keywords within a topic, which is sorted based on their contribution. The vertical axis, "c-TF-IDF score," represents the contribution of keywords to the composition of a topic.</p

Topic information mined by the BERTopic algorithm (N = 17,593).

Topic information mined by the BERTopic algorithm (N = 17,593).</p

Secondary topic similarity matrix diagram.

The figure displays the similarity between secondary topics. In the heatmap, the depth of the color represents the level of similarity: a darker color indicates a higher similarity, meaning the compared pair of topics are more similar in content; conversely, a lighter color signifies a lower similarity, indicating greater content differences between the topics being compared.</p

Raw data.

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.</div

The distribution of the number of letters as a percentage among responding departments.

The distribution of the number of letters as a percentage among responding departments.</p

2D Distribution of secondary topic documents and their topics.

The figure presents the distribution of secondary topic documents and their corresponding topics within a two-dimensional space, enabling rapid identification of the clustering of topics within the document set and the relative positioning of topics. This allows for the analysis of potential connections or differences between topics. Different colored blocks within the diagram represent distinct topics, with the list on the right detailing the specific content of each numbered topic. The spatial distribution of colored blocks signifies the degree of relatedness between document topics; blocks in close proximity indicate similar topic content, while those further apart suggest greater differences in topic content.</p

Secondary topic distribution diagram.

The figure displays the distribution of secondary topics within a two-dimensional scaling space.</p

Primary topic distribution diagram.

The figure displays the distribution of primary topics within a two-dimensional scaling space.</p

Frequency of keywords "Reflective Content" in letters.

Frequency of keywords "Reflective Content" in letters.</p