# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 5 Discussion
The polysemy and context dependence of personality-related text likely contributed to the system's performance challenges. Nevertheless, the F1-Score of 0.81 and minimal discrepancy between precision and recall indicate stable and reliable classification results overall. For the appearance category, the system exhibited outstanding performance, with an accuracy of 0.98, a precision of 0.86, a recall of 0.87, and an F1-Score of 0.86. The accuracy of 0.98 highlights the system's ability to accurately classify nearly all appearance-related counseling data. The precision and recall were also excellent, at 0.86 and 0.87, respectively. Given that words related to appearance tend to focus on specific external features and are less likely to overlap with other categories, the high performance in this category is indicative of the system's reliability. The family category achieved an accuracy of 0.84, a precision of 0.91, a recall of 0.84, and an F1-Score of 0.87. The high precision of 0.91 indicates that the system accurately classified family-related counseling data. With a recall of 0.84, the system correctly predicted 84% of the actual family-related data. The F1-Score of 0.87 suggests that the system achieved a good balance between precision and recall for this category. Family-related topics are typically well-defined in counseling data, contributing to the system's strong performance. In summary, the proposed system achieved the highest performance in the appearance and school categories, while relatively lower performance was observed in the friends category. The discrepancy between precision and recall in the friends category suggests the need for additional data training and improvement, particularly considering the data imbalance or characteristic differences. For all categories except friends, the F1-Scoreexceeded 0.80, confirming the overall strong classification and analysis performance of the system. This study aimed to develop a system for the real-time automatic classification of non-face-to-face counseling data, enabling counselors to analyze and respond promptly. While prior research has mainly focused on analyzing themes in university counseling, identifying trends in academic counseling, and examining emotional shifts or keyword categorization, this study emphasizes real-time data analysis and the integration of emotional context to support quick decision-making by counselors. Distinguishing itself, this study combines the TF-IDF and Word Embedding techniques to automatically classify counseling data into five categories: school, friends, personality, appearance, and family. This combination enhances the system's ability to reflect emotional characteristics and semantic relationships among topics, offering new possibilities for analyzing emotional and psychological aspects not extensively explored in prior research. By applying an analysis method optimized for multidimensional counseling data with emotional context, this study demonstrates the feasibility of precise classification. Additionally, while existing studies often utilize techniques like Latent Dirichlet Allocation (LDA) or TF-IDF for general document classification or trend analysis, this study optimizes these techniques specifically for counseling data, achieving high accuracy in categorizing the counseling dataset. This approach offers novel potential for capturing relationships between emotional characteristics and themes in non-face-to-face counseling data. Future research could focus on further improving the system's accuracy and reliability through comparisons with advanced NLP models, such as BERT, and enhancing its practical utility by incorporating feedback from real counseling sessions. This study, therefore, serves as foundational research in counseling data analysis, demonstrating that text mining can effectively support counselors in providing prompt and accurate assistance.