Text-Mining-Based Non-Face-to-Face Counseling Data Classification and Management System

This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in...

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Bibliographic Details
Published in:Applied sciences 2024-11, Vol.14 (22), p.10747
Main Authors: Park, Woncheol, Oh, Seungmin, Park, Seonghyun
Format: Article
Language:English
Subjects:
Analysis
Computational linguistics
Counseling
counseling data
Data analysis
Data mining
Electronic health records
Emotions
Language processing
Mineral industry
Mining industry
Natural language interfaces
non-face-to-face counseling
Psychiatric services
Python
Social networks
text mining
TF-IDF (Term Frequency–Inverse Document Frequency)
Trends
User interface
Verbal communication
Word Embedding
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Summary:This study proposes a system for analyzing non-face-to-face counseling data using text-mining techniques to assess psychological states and automatically classify them into predefined categories. The system addresses the challenge of understanding internal issues that may be difficult to express in traditional face-to-face counseling. To solve this problem, a counseling management system based on text mining was developed. In the experiment, we combined TF-IDF and Word Embedding techniques to process and classify client counseling data into five major categories: school, friends, personality, appearance, and family. The classification performance achieved high accuracy and F1-Score, demonstrating the system’s effectiveness in understanding and categorizing clients’ emotions and psychological states. This system offers a structured approach to analyzing counseling data, providing counselors with a foundation for recommending personalized counseling treatments. The findings of this study suggest that in-depth analysis and classification of counseling data can enhance the quality of counseling, even in non-face-to-face environments, offering more efficient and tailored solutions.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142210747