# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 6 Conclusions  
In this study, we propose a system that automatically classifies counseling data into predefined categories using text-mining techniques and evaluates its performance. By optimizing and applying existing text-mining methods to counseling data, we achieved more refined classification results, particularly through the combination of TF-IDF and Word Embedding. The classification performance evaluation showed high accuracy and F1-Scores across the predefined categories (school, friends, personality, appearance, family), indicating that the system effectively captures the multidimensional characteristics of counseling data. Notably, the system's strong performance in emotionally and subjectively nuanced categories like \"appearance\" and \"personality\" underscores the practical utility of this approach. The system proposed in this study is designed to automatically analyze non-face-to-face counseling data, allowing for the rapid classification of key emotional elements and psychological states that may require counselor intervention. In real counseling settings, this system has the potential to provide practical support to counselors, contributing to counseling efficiency and the provision of customized responses. Specifically, in terms of efficient counseling time utilization, the system automatically categorizes the primary topics and emotional states within counseling data, enabling counselors to quickly identify essential information. This allows counselors to focus on enhancing the quality and effectiveness of counseling while reducing the time spent on data analysis. The system also holds promise for improving consistency and quality in counseling responses. Based on the categorized data, counselors can develop structured response strategies for issues such as interpersonal relationships, family problems, and self-esteem concerns, providing reliable analysis results that support more effective counseling, especially for novice counselors. Furthermore, by analyzing counseling data to recommend optimized counseling directions for each individual, the proposed system demonstrates the potential for offering tailored solutions. For example, if \"friendship\" and \"self-esteem\" are identified as primary counseling topics, the system can automatically suggest tailored counseling strategies to deliver appropriate guidance to the client. As the volume of counseling records grows with the increase in non-face-to-face counseling, this system is well suited for processing and analyzing large-scale counseling data. This enables the efficient management and analysis of multiple client records, facilitating data-driven decision-making in counseling environments and ultimately enhancing the overall quality of counseling. A limitation of this study is that the dataset used was limited to a specific age group (8-19 years), which may have restricted the generalizability of the results. Future research will expand by incorporating data from various age groups and demographic backgrounds to improve the model's generalizability. To address misclassification issues observed in the \"Friends\" category, future work will explore the implementation of multi-label or hierarchical classification methods to more accurately capture the overlapping characteristics among categories. Although this study trained the system on a somewhat limited dataset of 761 records, future efforts will involve leveraging a larger volume of counseling data and applying diverse techniques to reduce misclassification and further enhance classification performance. In conclusion, this study demonstrates the potential of applying text-mining techniques to counseling data and marks an important initial step toward automated text analysis in counseling. Future research should utilize more diverse age groups and larger datasets to improve the system's generalizability. Additionally, the in-depth exploration of various text-mining techniques and classification algorithms will be crucial for further performance enhancement.