## 4 Case Study  
The dataset utilized for our experiments contained 2310 issues extracted from the \"_Have your say_ ([[https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13750-Hazardous-chemicals-prohibiting-production-for-export-of-chemicals-banned-in-the-European-Union_en,accessed](https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13750-Hazardous-chemicals-prohibiting-production-for-export-of-chemicals-banned-in-the-European-Union_en,accessed)]([https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13750-Hazardous-chemicals-prohibiting-production-for-export-of-chemicals-banned-in-the-European-Union_en,accessed](https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13750-Hazardous-chemicals-prohibiting-production-for-export-of-chemicals-banned-in-the-European-Union_en,accessed)) on 5 March 2024)\" platform. The issues concerned a discussion initiated about hazardous chemicals and the potential prohibition of exporting chemicals banned in the European Union. Each instance contained the feedback provided by the users, as well as important metadata, such as the date of creation, the username, the country of origin, etc. We primarily focused on the 'englishfeedback' column that contained the textual data that we utilized for the proposed approach. In our experiments, we made the following assumptions: (i) we disregarded instances that contained 10 or less words, thus filtering out noisy data points; (ii) we removed duplicated comments, as they might lead to overfitting issues without adding more information; (iii) we removed highly similar texts, to ensure the robustness of our method. The statistical information concerning the textual attributes of the dataset at hand is summarized in Table 1. As illustrated in Figure 1, the vast majority of the corpus comprised short-text documents (i.e., ranging from 10 to 100 words), while 25% of the documents had a body of less than 41 words. These observations indicate that the dataset contained rather short-text samples. More important, however, was the fact that there existed a uniformity across the original and the pre-processed dataset, which enabled the employment of standard clustering algorithms. Figure 1 illustrates how the number of words was distributed across the totality of the corpus at hand. Figure 1: _Cont._  
As far as clustering was concerned, we employed a _Silhouette_ and _Inertia_ plot to determine the most suitable algorithm as well as the number of clusters we should create. _Silhouette_ quantifies the consistency within the cluster data. Specifically, the _Silhouette_ value takes into account the similarity of an instance when compared with instances from the same cluster (i.e., cohesion) as well as the similarity to instances from other clusters (i.e., separation). The value obtains ranges from \(-1\) to \(+1\), with high values indicating better clustering quality. To calculate the _Silhouette_ value for an instance \(i\), the mean intra-cluster distance \(a(i)\) (Equation (8)) and the mean nearest-cluster distance \(b(i)\) (Equation (9)) must be calculated. Then, the _Silhouette_ score for the instance at hand is calculated, using Equation (10), while the overall _Silhouette_ score is calculated by Equation (11). \[a(i)=\frac{1}{|C_{i}|-1}\sum_{j\in C_{i}}\sum_{j\
eq i}dist(d_{i},d_{j}) \tag{8}\]  
\[b(i)=\min_{C_{k}\
eq C_{i}}\frac{1}{|C_{k}|}\sum_{j\in C_{k}}dist(d_{i},d_{j}) \tag{9}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Word Statistics**} & & \\ \hline  & **\# Documents** & **Min** & **Max** & **Average** & **STD** & **Median** & **25\%** & **75\%** \\ \hline
**Original Dataset** & 2310 & 2 & 592 & 100.93 & 95.16 & 69 & 40 & 121 \\ \hline
**Pre-Processed Dataset** & 2177 & 10 & 592 & 99.43 & 92.47 & 70 & 41 & 118 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the documents of the datasets.