# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 1 Introduction  
Counseling has become an essential area in modern society. Particularly after the pandemic, the development of non-face-to-face communication methods and digital platforms has led to a rapid increase in the demand for online counseling. Many individuals experiencing psychological problems or emotional difficulties prefer non-face-to-face counseling, which offers greater anonymity and comfort, compared to traditional face-to-face methods. However, expressing emotions and situations accurately, as one might expect from machines, presents challenges. While some individuals may clearly articulate their emotions, many struggle to convey their thoughts and feelings properly. This difficulty often leads to a lack of self-awareness regarding their emotional state. Non-face-to-face counseling also presents limitations for counselors. The inability to observe non-verbal cues, such as facial expressions or tone of voice, makes it challenging to fully comprehend the client's emotional and psychological state. Consequently, this can hinder the counselor's ability to understand the client's inner self and accurately assess their feelings. A lack of counselor expertise is another issue often highlighted. Clients may receive limited information when consulting with counselors who lack extensive knowledge or experience in counseling [1]. Volunteer counselors, in particular, may struggle to provide effective counseling, as many have undergone only short-term training. This lack of professionalism often leads to ineffective counseling outcomes. In fact, only 9.3% of volunteer counselors continue to provide counseling for more than five years. Thus, professional support, such as workshops and supervision, is essential for sustaining volunteer counseling efforts [2; 3; 4]. On the other hand, long-term volunteers demonstrate greater confidence and satisfaction in their counseling abilities [5]. Non-face-to-face counseling does offer benefits, including accessibility,convenience, and anonymity, that allow clients to share their issues more openly without fear of judgment. This environment often encourages more honest emotional expression. However, anonymity can also impede trust-building between clients and counselors, potentially leading to conflicts or a lack of objectivity and fairness in the counseling process due to mistrust of the counselor [6]. Despite the advantages of anonymity and convenience, non-face-to-face counseling faces challenges regarding the lack of expertise and continuity, limiting its effectiveness in delivering quality counseling services. To address these issues, this paper proposes a novel non-face-to-face counseling system. The proposed system uses text-mining techniques to analyze the psychological state of clients, even when they struggle to articulate their emotions or problems clearly in a non-face-to-face setting. Text mining serves as a tool for analyzing the conversation data between clients and counselors to detect emotions and psychological states. Through this approach, counselors can infer unexpressed emotions and provide more accurate assessments. The process of this study involves several key steps. First, the counseling data are collected and preprocessed. Next, essential information is extracted from the text using TF-IDF (Term Frequency-Inverse Document Frequency) and Word Embedding techniques. These methods help identify meaningful patterns and emotional states within the client's conversation. The data are then automatically classified into predefined categories, allowing the system to search the database for the most appropriate counseling data. This provides counselors with customized solutions and the most suitable therapeutic recommendations based on the client's situation. These insights can significantly enhance the quality of counseling and offer more effective and personalized solutions for clients.