## 6 Conclusions  
In this study, we analyzed various embedding models applied to text mining within e-participation platforms, specifically focusing on iVoting and Join. Our findings reveal that the all-MiniLM-L6-v2 embedding demonstrated the highest efficacy in capturing meaningful semantic relationships through improved NPMI scores, despite incurring higher computational costs. Conversely, embeddings such as BAAI/bge-base-en-v1.5 and BAAI/bge-small-en-v1.5 exhibited lower performance metrics but offered a more efficient computational profile, making them viable alternatives for applications requiring speed and resource efficiency. Additionally, this study has shown that the proposed pipeline is also applicable to other datasets such as BBC [29] and that the proposed pipeline can obtain valuable insights on e-participation platforms while overcoming the language barriers present in iVoting and Join.  
This analysis underscores the necessity for practitioners and researchers in the field of e-participation to carefully consider their choice of embedding models based on the specific context and objectives of their text-mining efforts. As e-participation platforms increasingly play a role in democratic governance, the insights derived from effective text-mining techniques can significantly enhance the understanding of public sentiment and the thematic evolution of proposals over time. Future research should continue to explore advanced embedding techniques and hybrid models to refine the balance between performance and computational efficiency, ultimately contributing to more effective public engagement and decision-making processes.  
Despite these contributions, several limitations remain. First, the presence of noise in the dataset poses challenges in accurately classifying proposals, and future work should explore methods to refine classifications with greater precision. Second, LLMs generate unfiltered text, which may introduce ambiguous or politically sensitive topic labels. Addressing this issue requires incorporating filtering mechanisms to improve the interpretability of generated topics. Third, proposals often exhibit semantic overlap, meaning they can belong to multiple categories simultaneously. This ambiguity affects both traditional clustering approaches and soft clustering techniques. Further research is needed to develop evaluation metrics that can effectively assess topic models in cases where soft clustering is necessary.  
M.S.: Investigation, Methodology, Software, Visualization, Writing--Original Draft. J.F.: Writing--Review and Editing, Investigation, Methodology, Conceptualization. S.-Y.C.: Conceptualization, Supervision, Resources, Funding Acquisition, Project Administration. Y.-L.K.: Supervision, Resources. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by two institutions: (1) The National Science and Technology Council of the Republic of China (Taiwan) under grant NSTC 110-2221-E-011-136-MY3 and the Smart Manufacturing Innovation Center as part of the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Science and Technology (MOST) in Taiwan under grant 109-2221-E-011-069. (2) By the German Federal Ministry of Education and Research (BMBF) funded project \"Worldmaking from a Global Perspective: A Dialogue with China\".  
Not applicable.  
Some of the data are publicly available; other data and the code for the experiments will be made available by the authors.  
We would like to thank JOIN and iVoting for publishing the data and therefore enabling us to conduct this study. This article is a revised and expanded version of a paper entitled \"Text-Mining of E-Participation Platforms: Applying Topic Modeling on Join and iVoting in Taiwan\", which was presented at TE2024: Engineering for Social Change, London, UK, 9-11 July 2024.  
The authors declare no conflicts of interest.