## 4 Results  
### Training Experiments  
This section presents the outcomes of the training experiments conducted with the three topic modeling techniques: LDA, NMF, and BERTopic. Each model was evaluated across four different data formats: original articles without any pre-processing, articles after cleaning, articles after cleaning and stemming with the ISRI stemmer, and articles after cleaning and stemming with the Tashaphyne stemmer. 1. LDA Experiments: The results of the LDA experiments are summarized in Table 1. * Original Articles without Pre-processing: This baseline experiment resulted in a low coherence score (0.012) and moderate topic diversity (0.571). The model exhibited high perplexity (17,652.513), indicating difficulty in predicting the unseen data, as well as the longest training time (649.00 s). * Cleaning: After cleaning the text, the coherence score improved significantly to 0.040 and topic diversity increased to 0.729. The perplexity decreased to 14,655.042 and the training time was reduced to 380.82 s, reflecting the benefits of removing noise from the data. * Cleaning + Stemming (Tashaphyne): This configuration achieved the highest coherence score (0.051) among the LDA experiments, with a slight decrease in topic diversity (0.724). The perplexity dropped dramatically to 2836.581 and the training time decreased further to 289.69 s. This indicates that Tashaphyne stemming effectively enhanced the model's interpretability and prediction accuracy. * Cleaning + Stemming (ISRI): While this setup resulted in a slightly lower coherence score (0.037) compared to Tashaphyne, it achieved the lowest perplexity (1424.293) among the LDA experiments, suggesting superior predictive performance. The training time was similar to the Tashaphyne setup, at 289.51 s.  
Among the LDA experiments, Experiment 3--with basic cleaning and stemming using Tashaphyne--achieved the highest coherence score (0.051) and good topic diversity (0.724). However, when considering predictive performance, Experiment 4--cleaning + stemming (ISRI)--stood out with the lowest perplexity. This suggests that, while stemming using Tashaphyne can enhance the interpretability of topics, incorporating ISRI stemming significantly improves the model's ability to predict and generalize to unseen data. 2. NMF Experiments: NMF was also tested under similar pre-processing configurations, with the results summarized in Table 2. * Original Articles without Pre-processing: The NMF model initially achieved a coherence score of 0.032 and a lower topic diversity (0.557) than LDA. The training time was 2741.79 s, indicating that the model struggled with raw data. * Cleaning: After cleaning, the coherence score improved to 0.038 and the topic diversity increased to 0.624. The training time significantly decreased to 1084.76 s, demonstrating that cleaning had a substantial positive impact. * Cleaning + Stemming (Tashaphyne): This configuration resulted in the highest coherence score (0.071) and the best topic diversity (0.714) across all NMF experiments. The training time was further reduced to 528.50 s. The Tasha \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Pre-Processing** & **NPMI Coherence Score \(\uparrow\)** & **Topic Diversity** & **Perplexity** & **Training Time** \\ \hline Original articles (without pre-processing) & 0.012 & 0.571 & 17,652.513 & 649.00 \\ \hline Cleaning & 0.040 & 0.729 & 14,655.042 & 380.82 \\ \hline Cleaning + stemming (Tashaphyne) & **0.051** & 0.724 & 2836.581 & 289.69 \\ \hline Cleaning + stemming (ISRI) & 0.037 & 0.643 & 1424.293 & 289.51 \\ \hline \hline \end{tabular}  
* **Note:** Bold value indicates the best NPMI score among the compared configurations.  
\end{table} 
Table 1: Summary of experimental results for the Latent Dirichlet Allocation (LDA) model. diversity of 0.886. The original articles (without pre-processing) had slightly higher coherence (0.126) but lower diversity (0.843).