## 3 Materials and Methods
### Research Method
#### 3.2.4 Concept of LDA Topic Modeling
This approach yields a more accurate measure of topic coherence by considering the relationships between neighboring words rather than simply measuring direct word similarity [44]. Figure 4: Conceptual model of LDA topic modeling. The \(C_{v}\) coherence score is used to evaluate the quality and interpretability of topics generated by LDA. It is computed as follows:  
\[C_{v}=\frac{1}{N\times K}{\sum_{k=1}^{K}{\sum_{n=1}^{N}{s\text{cos}\left({\overset {\rightarrow}{w_{n,k}}\cdot\overset{\rightarrow}{w_{k}^{\ast}}}\right)}}} \tag{1}\]  
* \(C_{v}\): Coherence score used to evaluate the quality and interpretability of topics. * \(K\): The number of topics in the LDA model. * \(N\): The number of words per topic used for coherence calculation. * \(s\cos\left({\overset{\rightarrow}{w_{n,k}}\cdot\overset{\rightarrow}{w_{k}^{ \ast}}}\right)\): Cosine similarity function measuring pairwise similarity between word vectors. * \(\overset{\rightarrow}{w_{n,k}}\): Context vector representing the co-occurrence frequency of a keyword within a topic. * \(\overset{\rightarrow}{w_{k}^{\ast}}\): Reference vector used for computing coherence based on word distributions. To determine the optimal number of topics for LDA modeling, the parameters were set following the methodology outlined by [PERSON] et al. [32]. Specifically, \(\alpha\) was set to 0.1, \(\beta\) to 0.01, and the number of iterations to 1000. The number of topics was gradually increased from two to fifteen to evaluate the coherence scores, a critical metric for assessing the interpretability of the topics generated by the model. Building upon this parameter selection process, we implemented LDA for topic modeling in this study. The implementation process followed a structured approach to ensure meaningful topic extraction from the dataset. First, all documents were converted to lowercase, special characters were removed, and stop words were filtered out to eliminate non-informative terms. Tokenization was performed to split the text into individual words, and lemmatization was applied to standardize word forms across the dataset. Next, the text corpus was transformed into a document-term matrix (DTM) using TF-IDF weighting, which helped to emphasize important terms while reducing the impact of highly frequent but less meaningful words. LDA was then trained on this representation, modeling each document as a mixture of topics and each topic as a probability distribution over words. The Gibbs sampling algorithm was used for estimating topic distributions. To enhance the reliability of topic modeling, the alpha (\(\alpha\)) and beta (\(\beta\)) parameters were fine-tuned within a range of 0.01 to 0.99. The selection of hyperparameters and the evaluation of coherence scores were conducted following established methodologies to ensure the interpretability and stability of the extracted topics. Finally, the extracted topics were analyzed based on their most representative keywords. To ensure the validity of the topics, domain experts reviewed the results and assessed their relevance to key research areas in LLM security. This structured approach provided a clear understanding of emerging security themes in generative AI research.