# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 2 Related Works (Text Mining)  
As information technology advances and the volume of data continues to grow, it has become increasingly essential to develop methods that can process large amounts of unstructured data quickly and accurately. The need for the rapid extraction of meaningful information from these data is critical as the speed of data flow increases [7; 8; 9]. Traditional data analysis methods typically rely on users directly analyzing and interpreting data, which introduces the risk of subjective bias and is not well suited for handling massive datasets efficiently. However, text-mining technology can overcome these limitations by classifying large datasets into various topics, extracting key information, and allowing for deeper insights into each topic [10; 11]. Text mining is a technique that extracts valuable information from unstructured or text-based data, leveraging NLP (Natural Language Processing) to uncover correlations, classify data, and summarize key findings. This technology has broad applications across numerous fields [12; 13; 14]. It incorporates methods such as NLP, information retrieval, document classification, clustering, text link analysis, and term extraction [15]. As the volume of web-based data rapidly increases, the importance of text mining has been amplified, particularly as it helps structure and analyze semi-structured text data, visualize results, and uncover new insights. As a result, text mining has gained widespread attention in research, highlighting its increasing significance [16; 17]. With improvements in accuracy, the scope of text-mining applications has expanded across various domains. It is used to classify large volumes of documents, extract representative keywords, perform large-scale document searches, index and recommend content, and explore specific topics [18; 19; 20]. For example, studies using LDA (Latent Dirichlet Allocation) topic modeling include a case search and classification system designed to assist the general public in searching case law [21], a methodology for visualizing digital twin environments [22], and an analysis of online news articles aimed at identifying social issues related to patient safety [23]. Additional research has focused on predicting genes associated with diseases and uncovering new gene-disease relationships [24] and on using text mining to suggest rare diseases based on patient symptoms to aid in diagnosis [25]. Moreover, text mining has been applied to social media data for various purposes, such as analyzing customer needs [26; 27; 28; 29] and assessing emotional changes to predict suicide risk [30; 31; 32; 33]. While research in applying text-mining techniques has expanded across various fields, studies specifically focused on counseling data remain in their early stages, presenting challenges in direct comparison with existing studies. Nonetheless, studies with similar approaches include those analyzing mental health and predicting suicide risk through EHRs (Electronic Health Records) data. For example, a UK NHS study highlighted that relying solely on administrative codes could overlook up to 83% of patient records at risk for suicide, emphasizing the value of text-mining techniques to more precisely identify risk factors [34]. Additionally, research utilizing social media data for suicide risk prediction has demonstrated the effectiveness of text mining in detecting individual risks or tracking trends within groups through real-time data analysis [31]. In counseling-related research, some studies have used text mining to analyze themes and trends in university counseling programs, systematically categorizing major counseling areas through clustering methods to identify primary topics within these programs [35]. In the academic counseling domain, topic modeling techniques, such as Latent Dirichlet Allocation (LDA), have been employed to identify frequently occurring keywords and research topics, analyzing their frequencies to reveal key areas of interest [36].