# Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System
## 5 Discussion  
In this study, we developed a system that automatically classifies counseling data into five categories school, friends, personality, appearance, and family using text-mining tech \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline School & 0.88 & 0.86 & 0.88 & 0.87 \\ \hline Friend & 0.80 & 0.75 & 0.80 & 0.77 \\ \hline Personality & 0.80 & 0.82 & 0.80 & 0.81 \\ \hline Appearance & 0.98 & 0.86 & 0.87 & 0.86 \\ \hline Family & 0.84 & 0.91 & 0.84 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of classification performance evaluation by measuring Accuracy, Precision, Recall, and F1-Score by category. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **School** & **Friend** & **Personality** & **Appearance** & **Family** \\ \hline School & 44 & 3 & 1 & 2 & 0 \\ \hline Friend & 4 & 40 & 2 & 3 & 1 \\ \hline Personality & 2 & 4 & 40 & 1 & 3 \\ \hline Appearance & 1 & 4 & 2 & 49 & 0 \\ \hline Family & 0 & 2 & 4 & 2 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: These are the results of deriving the confusion matrix for each category. niques, and we evaluated its performance. The experimental results indicated satisfactory overall system performance, with the highest accuracy observed in the appearance and school categories. However, relatively lower performance was observed in the friends and personality categories, suggesting the need for additional refinement. The performance of the school category was notably high, with an accuracy of 0.88, a precision of 0.89, a recall of 0.88, and an F1-Score of 0.87. With both precision and recall exceeding 0.88, it is evident that the system achieved a balanced rate of correct predictions for school-related data and a high level of accuracy for the predicted category. The F1-Score of 0.87, along with consistent precision and recall, indicates that school-related counseling data have distinct patterns that were effectively captured, leading to minimal confusion with other categories. The clear distinction of school-related terms, such as \"study\", \"exam\", and \"school life\", allowed the system to perform stable classification for this category. In contrast, the performance in the friends category was lower, with an accuracy of 0.80, a precision of 0.75, a recall of 0.80, and an F1-Score of 0.77. Although the accuracy of 0.80 was relatively strong, the precision of 0.75 suggests that the system misclassified a significant portion of data as belonging to the friends category. This may be due to the overlap of friend-related issues with other categories, such as family relationships or personality, which caused classification ambiguity. Despite the recall of 0.80, indicating that 80% of actual friend-related data were correctly predicted, the overall F1-Score of 0.77 reflects the need for improvement in terms of accuracy. The counseling data in the \"Friends\" category frequently overlaps thematically with the \"Family\" or \"Personality\" categories. For example, cases where individuals rely on friends due to family conflicts or encounter friendship difficulties due to personality issues illustrate this overlap. Such thematic overlap introduces ambiguity when categorizing certain counseling data as \"Friends\" versus \"Family\" or \"Personality\". This overlap may lead to misclassification, where data intended for the \"Friends\" category are instead categorized under \"Family\" or \"Personality\". This ambiguity could contribute to the relatively lower classification performance (e.g., accuracy and precision) observed in the \"Friends\" category. The personality category demonstrated similar performance to the friends category, with an accuracy of 0.80, a precision of 0.82, a recall of 0.80, and an F1-Score of 0.81. Personality-related data often reflect clients' personal experiences or situations, making feature extraction more complex.