## 4 Experiments
### Number of Topic Effects  
In our previous experiment, we fixed the number of topics at 50. However, the performance of a neural topic model can be impacted by the number of topics. To investigate this, additional experiments were conducted by varying the number of topics from 20 to 100. The experimental results are shown in Figure 3.  
For the MRD dataset, neither ANTM nor ENTM appeared to consistently exhibit improved performance as the number of topics increased. ANTM exhibited its maximal performance when the number of topics was 80, whereas ENTM achieved its highest performance when the number of topics was 60. This suggests that for the MRD dataset, there is no linear relationship between the number of topics and model performance, indicating the need for careful selection of the topic number.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**ANTM Accuracy**} & \multicolumn{2}{c}{**ANTM Precision**} & \multicolumn{2}{c}{**ANTM Recall**} & \multicolumn{2}{c}{**ANTM F1**} \\ \hline  & \begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** & \
\begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** \\ \hline MRD & \(-4.2576\) & 0.0018 & \(-3.4281\) & 0.0048 & \(-4.7978\) & 0.0004 & \(-4.1896\) & 0.0011 \\ \hline
20 Newsgroups & \(-2.4514\) & 0.0300 & \(-2.9484\) & 0.0112 & \(-3.0977\) & 0.0082 & \(-2.9638\) & 0.0108 \\ \hline YELP & \(-4.6724\) & 0.0003 & \(-5.7028\) & 0.0001 & \(-5.5028\) & 0.0001 & \(-5.9682\) & 0.0001 \\ \hline
**Dataset** & \multicolumn{2}{c}{**ENTM Accuracy**} & \multicolumn{2}{c}{**ENTM Precision**} & \multicolumn{2}{c}{**ENTM Recall**} & \multicolumn{2}{c}{**ENTM F1**} \\ \hline  & \begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** & \begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** & \
\begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** &
\begin{tabular}{c} **Test** \\ **Statistic** \\ \end{tabular} & \(p\)**-Value** \\ \hline MRD & \(-2.2591\) & 0.0382 & \(-2.5513\) & 0.0226 & \(-2.6032\) & 0.0200 & \(-2.7763\) & 0.0139 \\ \hline
20 Newsgroups & 4.3831 & 0.0011 & 3.8788 & 0.0023 & 4.1705 & 0.0015 & 3.9874 & 0.0020 \\ \hline YELP & \(-2.5017\) & 0.0240 & \(-4.4778\) & 0.0004 & \(-4.4527\) & 0.0004 & \(-4.5023\) & 0.0004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of \(t\)-tests.  
For the 20 Newsgroups dataset, the performance of ANTM appeared to decrease slightly with 80 topics, being optimal with 30, 50, and 70 topics. For ENTM, performance was relatively high when the number of topics was 30, 50, 70, 80, and 100. This indicates that ENTM is more robust to changes in the number of topics than ANTM, which shows more variability.  
On the YELP dataset, ANTM achieved its best performance when the number of topics was 40. However, no significant differences in performance were observed for ENTM except when the number of topics was set at 70. This suggests that ENTM is less sensitive to the number of topics in the YELP dataset, whereas ANTM benefits from a specific number of topics.  
From these observations, it is evident that each model's performance changes as the number of topics varies across different datasets. For some datasets, significant performance fluctuations were observed with respect to changes in the number of topics, whereas for others, such variations were relatively negligible. Therefore, it is crucial to treat the number of topics as a hyperparameter and identify the optimal number of topics for each specific dataset.  
Figure 3: Visualizing the effect of the number of topics.  
Overall, our findings suggest that the number of topics significantly affects NTM performance; hence, careful tuning of this hyperparameter is essential for achieving optimal results across different datasets.