# A Neural Topic Modeling Study Integrating SBERT and Data Augmentation
## 5 Results and Analysis  
First, we evaluated the topic coherence metrics NPMI and C_V values, with the number of topics set to 50. As shown in Table 2, we can see that LDA performed the worst, with low scores in both NPMI and C_V metrics. LDA is a probabilistic topic model based on statistics. ProdLDA ranked second to last in NPMI and C_V values. It is a neural topic model that uses a variational autoencoder. The C_V value of SBERT + LDA was second only to ZeroShotTM, but the NPMI value was lower and in the fourth place. The ETM model had a higher NPMI value than LDA and ProdLDA, but its score was relatively low in the C_V metric. ZeroShotTM ranked second in the model list. Although it scored higher than the previous models in both metrics, it still had its drawbacks, which may be attributed to its lack of consideration for data sparsity issues. Its scores in both metrics were close to our model's scores. Our model performed the best in both metrics, thanks to our approach that overcame the data sparsity issue in text and enhanced the comprehensive information extraction ability. Thus, our model had a better topic quality. Then, we evaluated the diversity metrics of different topic models using the reciprocal of the RBO measure as the score, with the number of topics set to 20. The results are shown in Table 3. From the table, we can see that ProdLDA performed second worst in terms of diversity. Diversity measured the frequency of different words appearing in each topic, and its performance was average. The LDA model had the worst diversity performance due to its high word repetition rate in topics. ZeroShotTM had a similar diversity to that of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **NPMI** & **C\_V** \\ \hline Lda & \(-\)0.181 & 0.301 \\ ProdLDA & \(-\)0.143 & 0.361 \\ SBERT + LDA & \(-\)0.126 & 0.423 \\ ETM & \(-\)0.083 & 0.313 \\ ZeroShotTM & 0.011 & 0.441 \\ \hline
**Ours** & **0.036** & **0.467** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the topic coherence (NPMI and C_V values) of different models. our model. Our model exhibited a good diversity in short-text datasets, with the highest diversity metric score. Then, when the number of topics was set to 50, we compared the topic diversity scores of different models. The results are shown in Table 4. From the table, it can be seen that LDA performed relatively poorly, as it is a probability-based statistical model that is not as effective as the neural topic model in terms of performance. The topic diversity performance of the ProdLDA model was average, while the ZeroShotTM model was second only to our model. Our model still demonstrated a good topic diversity, with a higher score in topic diversity than other baseline models. Then, we performed ablation experiments, as shown in Table 5; we conducted further ablation experiments and demonstrated the variations in NPMI scores of our model. In this experiment, we employed the model (\"distiluse-base-multilingual-cased-v2\") with a dimension of 512 for SBERT(d), while SBERT(a) utilized the other module (\"all-mpnet-base-v2\") with a dimension of 768. For more details about the SBERT model, please refer to the paper [18]. As seen from the table, when a larger SBERT model was employed, the score increased by 0.7%. Furthermore, when we integrated the Keyword Easy Data Augmentation (KEDA) module and the BiLSTM-Att module on top of the SBERT model, the NPMI score increased by 1.9%. This improvement could be attributed to the adoption of the new data augmentation method and the use of the attention mechanism to filter out noisy words. The experimental results demonstrated the effectiveness of our proposed method. Next, we show some examples of topics extracted by the ZeroShotTM model in Table 6. First of all, we observe that in topic_id = 8, the topic should be about science and technology, while the other two topics are not very coherent and cannot be distinguished from the specific topic to be expressed.