Citizen Sentiment Analysis

Recently, the co-creation process between citizens and local governments has become increasingly significant as a mechanism for addressing administrative concerns, such as public facility maintenance, disaster response, and overall administrative improvement driven by citizen feedback. Social media platforms have been recognized as effective tools to facilitate this co-creation process. Compared to traditional methods like surveys and public comment solicitations, social listening is deemed superior for obtaining authentic and naturally articulated citizen voices. However, there is a noticeable lack of research concerning the gathering of opinions specifically related to municipal issues via platforms like X (Twitter). This study seeks to address this gap by presenting an original methodology for analyzing citizen opinions through the deployment of large language models. Utilizing these models, we introduce three distinct applications based on our framework, each considering a different opinion typology. We demonstrate that our approach enables the analysis and comparison of citizen sentiments across various cities in relation to common political issues, tailoring the analysis to diverse goal types. The results of this research not only contribute to the understanding of citizen engagement via social media but also provide valuable insights into potential applications of large language models for municipal-related opinion analysis