## 4 Experimental Results and Comparative Analyses
### Performance Evaluations
#### 4.2.3 Results of Hyperparameter Tuning  
We further tested our model with hyperparameter tuning, and the experimental results indicate that the optimized hyperparameters significantly enhanced the model's performance. After conducting random searches based on the DBCV value, the carefully selected parameters for the UMAP and HDBSCAN models brought about substantial improvements in the clustering quality.  
For the UMAP model, setting n_neighbors = 50, n_components = 50, min_dist = 0, and metric = 'cosine' enabled a more effective dimensionality reduction. The reduced-dimensional vectors retained the essential structure of the high-dimensional data and facilitated the subsequent clustering process. This was evident in the improved performance of the HDBSCAN model, which was able to identify more distinct and meaningful clusters.  
With the HDBSCAN model's parameters set as min_cluster_size = 100, min_samples = 10, metric = 'euclidean', and cluster_selection_method = 'leaf', the model could better handle the complex distribution of 6G patent data. It effectively separated the data into clusters, minimizing the number of misclassified outliers. As a result, the topic representation obtained from the c-TF-IDF step became more accurate and interpretable.  
In terms of the evaluation metrics, as shown in Table 5, the optimized model showed a remarkable increase in topic coherence. The top words selected for each cluster were more  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **NSTs** & **RISs** & **ISAC** & **ISAGSC** & **EAIs** \\ \hline Baseline & 0.5968 & 0.6849 & 0.6775 & 0.6542 & 0.6647 \\ \hline PatentSBerta & 0.5860 & 0.6813 & 0.6739 & 0.6480 & 0.6645 \\ \hline SciBert & 0.5999 & 0.6735 & 0.6633 & 0.6508 & 0.6733 \\ \hline Roberta & 0.5922 & 0.6703 & 0.6755 & 0.6473 & 0.6724 \\ \hline \hline \end{tabular}
\end{table}
Table 4: This table shows the comparison of performances of averages of TD in different models. The elements highlighted in red in the table represent the maximum values in the corresponding columns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **NSTs** & **RISs** & **ISAC** & **ISAGSC** & **EAIs** \\ \hline Baseline & 0.4900 & 0.4978 & 0.4749 & 0.4683 & 0.4615 \\ \hline PatentSBerta & 0.4823 & 0.4953 & 0.4771 & 0.4768 & 0.4532 \\ \hline SciBert & 0.4875 & 0.5090 & 0.4812 & 0.4709 & 0.4571 \\ \hline Roberta & 0.4848 & 0.4967 & 0.4835 & 0.4675 & 0.4615 \\ \hline \hline \end{tabular}
\end{table}
Table 3: This table shows the comparison of performances of averages of TC in different models. The elements highlighted in red in the table represent the maximum values in the corresponding columns.  
closely related semantically, with the average topic coherence (TC) value increasing by approximately 40%, on average, across all datasets compared to the non-optimized model. This improvement in TC indicates that the model better captured the underlying concepts within each cluster.  
Simultaneously, the topic diversity (TD) also witnessed a positive change, as shown in Table 6. The proportion of unique words in the top-ranked words of each topic increased, resulting in a more diverse set of topics. This was particularly beneficial for comprehensively representing the various technological aspects in the 6G patent data. The TD value increased by about 30%, on average, suggesting that the hyperparameter-tuned model could generate more distinct and non-redundant topics.  
In summary, the hyperparameter tuning process was crucial for achieving better performance in our 6G technology DTM model. It provided a more refined way of handling patent data in a complex landscape, leading to more accurate topic representation and a deeper understanding of the emerging trends in 6G technology. Future research could explore more advanced hyperparameter optimization techniques, such as random searches or Bayesian optimization, to further improve the model's performance and generalization ability.