## 3 Materials and Methods
### Clustering Methods Used in the Research
HDBSCAN does not require the specification of a global density threshold; instead, it automatically identifies clusters at varying densities by constructing a dendrogram and applying a cluster extraction method based on the stability of clusters over different density levels. This results in a more robust and flexible clustering algorithm adapting to varying data distributions and densities. Nevertheless, both DBSCAN and HDBSCAN may suffer from high computational complexity, especially for large datasets, and are sensitive to the choice of a distance metric, which can significantly impact clustering performance. DBSCAN and HDBSCAN have been applied to various text analytical tasks, leveraging their ability to detect clusters of arbitrary shapes and varying densities. For instance, DBSCAN has been employed in topic modeling. It can identify coherent thematic groups within large collections of documents, improving the organization and retrieval of textual information. Similarly, HDBSCAN has been proven valuable in text summarization, enabling the extraction of representative sentences from a given document while maintaining the diversity of information and covering various aspects of the content. These applications demonstrate the versatility of density-based clustering methods in text analysis, offering unique advantages in handling complex data distributions and capturing nuanced relationships within textual data. BIRCH (balanced iterative reducing and clustering using hierarchies) is a hierarchical clustering method designed to efficiently process large datasets by constructing a tree structure called the clustering feature tree (CF-Tree) that captures the essential attributes of data points, such as their linear sum and squared sum [65]. The algorithm can manage large datasets by incrementally processing data points and adjusting the tree structure dynamically, significantly reducing computational complexity and memory requirements compared to the traditional hierarchical clustering methods. Recent applications of BIRCH in text analysis include document clustering. The method can group similar texts based on their feature representations, such as term frequency-inverse document frequency (TF-IDF) vectors. BIRCH has also been employed in analyzing customer reviews, enabling the identification of patterns and trends in customers' opinions, which can inform businesses about their strengths and areas for improvement. Furthermore, BIRCH has been utilized in social media analytics for event detection. The algorithm can cluster textual data from social media platforms to identify significant events or emerging discussion topics. These applications demonstrate the potential of BIRCH as an efficient and scalable clustering method for text analysis tasks, particularly in handling large-scale textual data. Affinity propagation is a clustering algorithm based on message passing that identifies a set of exemplar data points, which best represent the clusters, and groups similar data points around them [66]. Unlike many other clustering methods, affinity propagation does not require the number of clusters to be specified a priori, making it more adaptive to various data distributions. The algorithm works by iteratively exchanging real-valued messages between data points until their convergence, reflecting the suitability of each point to serve as an exemplar, and the preference of each point to select a specific exemplar. In the context of text analysis, affinity propagation has been applied to various tasks, such as document clustering, where it can effectively group similar documents based on their content or feature representations (e.g., TF-IDF vectors) [67]. Additionally, affinity propagation has been employed in analyzing social media data, such as community detection in social networks based on user-generated content, enabling the discovery of meaningful user relationships [68].