## 4 Results
### Topic Modeling Evaluation  
To ensure reproducibility, we provide the hyperparameter configurations for each topic modeling algorithm. For BoW-LDA, we used the default CountVectorizer (max_df = 0.9, min_df = 2, stop_words = 'english', max_features = 1000), and set n_components = 7, max_iter = 1000, learning_method = 'batch', random_state = 40. For TF-IDF-NMF, the vectorizer was TfidfVectorizer, with the same vectorizer configuration as used in BoW-LDA, and the model was initialized with init = 'nndsvd', n_components = 5, max_iter = 1000, and random_state = 40. For BERTopic, we used the \"all-MiniLM-L6-v2\" embedding model from Sentence Transformers. The dimensionality reduction was performed using UMAP (n_neighbors = 15, n_components = 5, min_dist = 0.0, metric = 'cosine', random_state = 40), followed by clustering with HDBSCAN (min_cluster_size = 8, min_samples = 3, and cluster_selection_epsilon = 0.4). The vectorization was handled with a default CountVectorizer, and we enabled calculate_probabilities = True for topic assignment probabilities. Table 1 shows the evaluation results of the three models, and each model has its own advantages. In terms of topic coherence, TF-IDF-NMF was better than the other two models, with the highest Topic Coherence in both groups (Group 0: 0.582, Group 1: 0.531). BoW-LDA was second (Group 0: 0.420, Group 1: 0.464). BERTopic was the lowest (Group 0: 0.417, Group 1: 0.489), indicating that it was relatively weak in the semantic aggregation of keywords. One possible reason is that although BERTopic can extract more dimensions, such as position, sentence meaning, etc., the conceptual distance of keywords in the topic may be farther than other models that simply consider similarity and are not concentrated enough. In terms of topic diversity, BERTopic performed best in both groups (Group 0: 0.925, Group 1: 0.967), indicating that the generated topics were quite different and had good diversity. TF-IDF-NMF also performed well (0.960 and 0.920); however, BoW-LDA performed worse (0.640 and 0.660), indicating that there was a lot of keyword overlap between different topics. BoW-LDA is based on the BoW model and is not context-aware. The topics it generates are likely to share high-frequency words, which are often reused by multiple topics, resulting in a high degree of overlap in keyword sets for different topics, thereby reducing Topic Diversity. In the comparison of semantic consistency indicators, BoW-LDA performed best (Group 0: 1977.158, Group 1: 1746.345), showing that its internal keyword aggregation was high, and its semantic consistency is strong. BERTopic is second (Group 0: 108.551, Group 1: 132.909), while TF-IDF-NMF performed the weakest (Group 0: 2.310, Group 1: 2.207), indicating that its topic clustering structure is the loosest among the three, and the keyword semantic distribution is the most dispersed. Compared with the generative or semantic embedding methods of LDA and BERTopic, NMF can only handle linear structures and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Group** & \begin{tabular}{c} **Topic** \\ **Coherence** \\ \end{tabular} & \
\begin{tabular}{c} **Topic** \\ **Diversity** \\ \end{tabular} &
\begin{tabular}{c} **Semantic** \\ **Consistency** \\ \end{tabular} & **Optimal K** \\ \hline BoW-LDA & Group 0 & 0.420 & 0.640 & 1977.158 & 7 \\ \hline  & Group 1 & 0.464 & 0.660 & 1746.345 & 7 \\ \hline TF-IDF-NMF & Group 0 & 0.582 & 0.960 & 2.310 & 5 \\ \hline  & Group 1 & 0.531 & 0.920 & 2.207 & 5 \\ \hline BERTopic & Group 0 & 0.417 & 0.925 & 108.551 & 4 \\ \hline  & Group 1 & 0.489 & 0.967 & 132.909 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of model evaluation. cannot model complex semantic boundaries or topic overlaps. This leads to discrete semantic distributions. In our framework, Topic Coherence and Diversity jointly evaluate the semantic quality of topics, focusing on keyword selection and uniqueness.