## 2 Research Methodology
### DMR (Dirichlet-Multinomial Regression) Topic Model  
Topic modeling is a statistical technique used to discover latent topics within a collection of documents [29]. These algorithms are particularly effective for analyzing large-scale \begin{table}
\begin{tabular}{c c} \hline \hline
**N-Gram** & **Examples** \\ \hline Uni-grams & This, is, not, a, pipe \\ \hline Bi-grams & This is, is not, not a, a pipe \\ \hline Tri-grams & This is not, is not a, not a pipe \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of N-gram. Figure 4: Result of topic coherence. data and can be applied not only to textual data but also to various types of information, including genetic sequences, images, and social networks, to identify underlying patterns [30]. DMR is an extension of LDA (Latent Dirichlet Allocation) topic modeling that incorporates document metadata characteristics (such as author, journal name, and publication date) as a third parameter in analyzing document and topic distributions [18]. Unlike traditional LDA, DMR topic modeling allows the prior distribution (\(\alpha\)) of topics to vary according to document-specific metadata characteristics [31]. This flexibility enables the topic distribution within documents to be influenced by metadata features such as publication date. The DMR topic model is particularly effective when analyzing how document characteristics affect topic distributions across different contexts. Due to these advantages, DMR topic modeling is being used in various recent studies [32; 33; 34]. As illustrated in Figure 5, the DMR model algorithm extends the LDA model by incorporating metadata (x) to derive topics based on specific attributes while maintaining the core functionality of deriving topic distributions within documents and word generation probabilities. In the DMR model, \(\mu\) is the mean of \(\lambda\) (hyperparameter), \(\sigma^{2}\) is the variance of the prior on parameter values, \(\alpha\) is the prior distribution over topics, x is metadata, \(\theta\) is topic proportions, z is a topic assignment for the nth word in document d, w is the nth word in document d, \(\varphi\) is the probability distribution over the vocabulary, and \(\beta\) is the Dirichlet prior on the topic-world distributions. The model follows a structured process: (1) extracting metadata from documents, (2) adjusting topic distributions based on metadata values, and (3) assigning words to topics similarly to LDA but with metadata-conditioned topic weights. This results in a metadata-aware topic modeling approach, which is particularly effective for analyzing how document characteristics influence topic distributions across different contexts. Sentiment analysis is another option to analyze user reviews. Sentiment analysis is a widely used technique for extracting emotional polarity from text, but it has several limitations. It often struggles with understanding context, handling ambiguity, and detecting sarcasm, leading to misclassification [35; 36]. Moreover, sentiment analysis typically relies on predefined categories (positive, negative, neutral), making it difficult to capture nuanced or mixed emotions [37]. Additionally, it is highly domain-dependent, requiring customized models for different industries or applications [38]. In contrast, the DMR topic model provides a more sophisticated approach to text analysis by extracting meaningful topics from documents while incorporating metadata for deeper insights [18; 39]. Unlike sentiment analysis, which primarily classifies emotions, DMR topic modeling identifies patterns across texts, allowing for contextual and thematic analysis. Furthermore, DMR is more effective for analyzing unstructured data, such as research papers or policy documents, where identifying sentiment may be less useful than discovering topic trends [40]. By leveraging quantitative modeling, DMR enables researchers to explore how topics evolve over time and across different contexts, making it a more robust tool for large-scale text analysis [41].