# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 3 Proposed Scheme
### Dataset  
Data Collection and Preprocessing Tasks  
The dataset used for data collection and training in the proposed system consists of 761 counseling records collected from clients aged 8 to 19 between 2017 and 2023, organized into text data. To enhance the system's training accuracy, the data were stored in CSV (Comma-Separated Values) format and used for system learning. The data used in this study were pre-classified into five categories--friends, personality, appearance, family, and others--according to the counseling topics provided by clients. For each category, key characteristics such as the number of data points, proportions within the dataset, predominant topics, and average conversation length are presented in Table 1. Table 1 provides a detailed summary of the main attributes of the counseling dataset used in this study. 1. Data Count and Proportion: Each category reflects clients' main areas of interest, with \"Friends\" representing the largest portion at 24.3% of the total data, followed by \"Personality\" and \"Appearance\" with similar proportions, and \"Family\" and \"Others\" trailing. This distribution highlights the predominant topics clients discuss during counseling. 2. Main Topics: Key counseling topics within each category were identified to capture representative characteristics. For instance, in the \"Friends\" category, frequent topics include friendship, conflict, and communication, while the \"Appearance\" category centers on themes such as appearance assessment, body shape, facial features, and insecurities. In the \"Others\" category, topics mainly involve academics and school-related concerns. 3. Conversation Length: Average, maximum, and minimum conversation lengths were analyzed by category, quantitatively reflecting the complexity of discussions. The \"Friends\" category has an average conversation length of about 180 words, with some dialogues extending to 400 words, indicating more in-depth discussions around relationships and conflicts. In contrast, the \"Family\" and \"Others\" categories have shorter average conversation lengths of 155 and 150 words, respectively, which may suggest more straightforward discussions on family and academic topics. A data refinement process was subsequently applied to the collected data. In the first purification process, spacing, blank spaces, and typographical errors in the CSV file were corrected. Morphological analysis and tokenization were performed using the Okt function, resulting in the extraction of 2314 nouns. Following this, a second purification process was applied to remove nouns with low analysis value or low relevance. In this step, the stopword list provided by the NLTK (Natural Language Toolkit) was referenced, and additional unnecessary words were included in the stopword list to remove terms lacking meaningful content. As a result, 646 nouns were extracted through the second purification process. These 646 extracted nouns were weighted by calculating the importance of each word using the TF-IDF technique. Table 2 below presents a portion of the results from the TF-IDF process. The words that achieved high TF-IDF scores were primarily related to interpersonal characteristics, such as \"friend\", \"courage\", \"best friend\", \"violence\", \"favor\", \"disregard\", \"pride\", and \"homosexuality\". To emphasize words with greater importance in the data, the tokenized words obtained from the initial data purification process were semantically vectorized using the Word2 Vec model. The previously calculated TF-IDF weights were then applied to calculate the Word Embeddings. In this study, the Word2 Vec model was trained with parameters set to vector_size = 100, window = 5, min_count = 1, and workers = 4, using the Skip-gram approach to capture the emotional context in counseling data more effectively. These settings allow the model to better capture semantic relationships between significant words within the dataset.