Category Classification of Text Data with Machine Learning Technique for Visualizing Flow of Conversation in Counseling

Abstract

The beginner counselors have more likely to continue counseling in their own interest, they have a high tendency to make great use of the closed-ended question in order to confirm the interpretation with the client. While expert counselors are instructing the counseling skill to beginner counselors, we consider that the reaction of a client for a beginner counselor's question is important to visualize in an appropriate method. To respond the request, we have developed a system for visualizing the flow of conversation in counseling. However, the expert counselor as the system user requires to correct the initial classification result manually, and the work burden is large, because the accuracy of the category classification of conversation data is very low in the current system. To improve this problem, we have implemented on the category classification method of text data with SVM (Support Vector Machine) as machine learning technique to visualize the flow of conversation in counseling. In addition, we have compared and evaluated with results of the initial classification method of the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the results in the current system.