# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
## 1 Introduction  
The rapid expansion of unstructured text data in digital environments poses significant challenges for information processing and knowledge extraction. The exponential growth of text content from platforms like online communities [1] and news reports [2] as well as user reviews [3] not only stimulates the demand for efficient data processing but also drives advancements in text mining techniques. Functioning as a robust analytical tool, text mining has significantly reshaped how textual information is processed and utilized [4]. By combining natural language processing (NLP) and data mining techniques, text mining facilitates the extraction of meaningful insights and relevant knowledge from large-scale text corpora; this enables essential operations like text classification, semantic recognition, and topic analysis.  
Nevertheless, traditional text mining approaches meet several challenges when dealing with complex semantic structures and large-scale textual data. Many conventional methodsrely heavily on manual rule-writing and method maintenance, which are not only time-consuming but also lack flexibility and scalability [5]. These constraints present significant challenges in terms of efficiency and accuracy, limiting their applicability in large-scale and dynamic text environments [6]. Moreover, existing text analysis workflows typically require the use of separate tools for text segmentation, topic modeling, and visualization. This causes users to switch environments and requires certain programming skills. This fragmented process seriously affects the efficiency of text segmentation, especially for users with limited technical background.  
To address these challenges, it is crucial to develop more adaptable and scalable methods to improve the efficiency and accuracy of text mining. Consequently, this study developed an integrated text mining application, WordMap, to solve this problem by integrating different functions into a unified and easy-to-access application, and this can improve the efficiency of text mining and the overall user experience. Firstly, WordMap integrates automatic text corpus segmentation and employs PKUSEG (for Chinese) and NLTK (for English) to support bilingual word segmentation tags. Secondly, it combines BERTopic for dynamic topic modeling to improve the accuracy of topic detection by capturing context and semantic nuances. Thirdly, WordMap also adds interactive visualization tools that allow users to intuitively explore text task results and topic relationships. By simplifying these processes into an integrated framework, it helps users reduce workflow fragmentation, improves usability, and lowers the technical barrier for conducting text analysis. The system is available at [[https://github.com/mpu-patrick-lab/wordmap](https://github.com/mpu-patrick-lab/wordmap)]([https://github.com/mpu-patrick-lab/wordmap](https://github.com/mpu-patrick-lab/wordmap)) (accessed on 11 June 2025).  
The organization of this paper is as follows: Section 2 provides an overview of previous studies on text mining techniques, including traditional text corpus segmentation and topic modeling methods. Section 3 outlines WordMap's system architecture, core methods, and evaluation approach. Section 4 presents the experimental results of specific applications, including functional demonstration and questionnaire results. Section 5 discusses the advantages of the integrated framework and its contributions to various application scenarios. Section 6 summarizes the research and proposes directions for future improvements.