# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
## 2 Literature Review  
Integrating text mining with deep learning has emerged as a critical research frontier for analyzing sophisticated unstructured data across domains. Early text segmentation methods mainly depended on rule-based strategies along with language-related features like syntactic structure [7], while traditional topic models like Latent Dirichlet Allocation (LDA) [8] and Non-Negative Matrix Factorization (NMF) [9] dominated semantic analysis. LDA modeled latent topics by using Dirichlet distributions, whereas NMF discovered themes through Non-Negative Matrix Factorization of term-document matrices [10]. However, traditional segmentation and topic modeling approaches exhibit limitations in adapting to domain-specific language and capturing nuanced semantics, thereby constraining their scalability in specialized contexts. These shortcomings have motivated the emergence of more advanced neural and hybrid models in recent years. From 2021 onward, hybrid approaches combining neural networks and topic modeling emerged. Researchers have integrated Top2 vec for topic modeling and RoBERTa for sentiment analysis to process over one hundred thousand news reports, which identified education, economy, and sports as key themes across five countries [11]. Recent research uses text mining for analyzing patient experience using large language models; however, it does not produce statistical results and visualizations automatically [12]. By conducting a large-scale news analysis, this process offers a noteworthy reference for understanding the coverage focus of different countries and provides data support for news communication research. [PERSON] et al. [PERSON] et al. (2013) proposed a user recommendation framework that combines Collaborative Topic Modeling (CTM) with collaborative filtering and generative adversarial networks to improve personalization recently. Their model effectively captures users' potential interests from text content and improves the accuracy of recommendations. In other domains, such as research into topic models and Artificial Intelligence, scholars have adopted the LDA topic model and Gibbs sampling technology for research [PERSON] et al. (2014). These methods have been employed for semantic extraction and latent topic discovery. They have also successfully analyzed relevant comments in medical forums and implemented topic classification and valid sentiment identification. [PERSON] explored the application of Artificial Intelligence and NLP in news platforms (NIP, 2015), conducting classification tasks and generating relevant content by analyzing social media trends and applying data mining methods. The developed system was able to generate news on similar topics through NLP analysis technology, which can effectively reduce the workload of journalists. In the social domain, other researchers have conducted studies in social media analytics research to make valuable predictions about public opinion and behavior by analyzing trends and sentiment on social media (NIP, 2015). As topic modeling techniques have advanced, researchers have begun to evaluate the performance of traditional and modern models across different domains. A recent comparative study by [PERSON] et al. empirically evaluated LDA, Top2 Vec, and BERTopic across several text datasets. Their results show that BERTopic consistently outperformed the other two models in terms of topic coherence and diversity, particularly in multilingual and semantically rich corpora ([PERSON], 2015). In addition, BERTopic has shown greater flexibility than models such as CTM in adapting to specific tasks. [PERSON] et al. introduced U-BERTopic, which incorporated urgency signals and contextual embeddings to detect emerging cyber-security threats ([PERSON] et al., 2016). The model achieved a higher accuracy and faster response compared to conventional topic models, and illustrated BERTopic's superior extensibility in dynamic, real-world applications.