## 4 Experiments
### Results  
To evaluate model performance, we utilized the F1 score, precision, recall, and accuracy as evaluation metrics, with each experiment conducted nine times with five epochs to ensure reproducible results. These metrics are commonly used to evaluate classification task performance. The accuracy is the ratio of correctly predicted instances to the total number of instances, whereas the F1 score is the harmonic mean of precision and recall. Here, precision is the ratio of true-positive instances to total instances predicted as positive, and recall is the ratio of true positive instances to total instances that should have been predicted as positive. The experimental results are listed in Table 3, where AVM and EVM denote the ALBERT and ELECTRA baseline models, respectively. The better results comparing the baseline model and the proposed model are highlighted in bold. The \"Time\" column indicates the average time required for each model to execute five epochs. In the case of ANTM, performance for all datasets improved by approximately 1-2% over that of the baseline model. This suggests that integrating a neural topic model with ALBERT can enhance model performance. In particular, ANTM exhibited consistent improvement in terms of AUC, precision, recall, and F1 score across all datasets, indicating its robustness. Moreover, the relatively small MRD dataset showed the most significant performance improvement, indicating that our proposed model is particularly effective \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \multicolumn{5}{c}{**MRD**} \\ \hline
**Evaluation** & **AUC** & **Precision** & **Recall** & **F1** & **Time** (s)** \\ \hline AVM & 0.6604 & 0.6348 & 0.6233 & 0.5940 & 244 \\ \hline ANTM & **0.6862** & **0.6593** & **0.6506** & **0.6213** & 262 \\ \hline EVM & 0.7303 & 0.7005 & 0.6922 & 0.6677 & 249 \\ \hline ENTM & **0.7390** & **0.7125** & **0.7021** & **0.6795** & 266 \\ \hline
**Dataset** & \multicolumn{5}{c}{**20 Newsgroups**} \\ \hline
**Evaluation** & **AUC** & **Precision** & **Recall** & **F1** & **Time** (s)** \\ \hline AVM & 0.8694 & 0.8026 & 0.8016 & 0.7972 & 853 \\ \hline ANTM & **0.8754** & **0.8121** & **0.8114** & **0.8070** & 929 \\ \hline EVM & **0.8948** & **0.8381** & **0.8372** & **0.8333** & 862 \\ \hline ENTM & 0.8883 & 0.8302 & 0.8295 & 0.8255 & 933 \\ \hline
**Dataset** & \multicolumn{5}{c}{**YELP**} \\ \hline
**Evaluation** & **AUC** & **Precision** & **Recall** & **F1** & **Time** (s)** \\ \hline AVM & 0.6830 & 0.5656 & 0.5743 & 0.5478 & 1666 \\ \hline ANTM & **0.6871** & **0.5751** & **0.5837** & **0.5567** & 1792 \\ \hline EVM & 0.7115 & 0.5956 & 0.6047 & 0.5787 & 1694 \\ \hline ENTM & **0.7145** & **0.6050** & **0.6124** & **0.5871** & 1809 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance results. in enhancing performance, especially when working with limited datasets and existing models alone. In contrast, although the ETNM model outperformed the baseline on the MRD and YELP datasets, it exhibited inferior performance to EVM on the 20 Newsgroups dataset. This discrepancy may be attributed to the specific characteristics of the 20 Newsgroups dataset, which might not benefit as much from NTM integration. In other words, since the existing model already achieves a high AUC of around 90%, incorporating neural topics into a well-performing model could slightly hinder its learning process. In Table 3, our proposed model generally outperformed the baseline models. To determine whether these performance differences were statistically significant, we conducted \(t\)-tests at a significance level of 0.05, with results presented in Table 4. The accuracy, precision, recall, and F1 score of ANTM had a \(p\)-value of less than the significance level of 0.05. Therefore, the performance difference between the ANTM and its corresponding baseline was statistically significant.