Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning
The beginner counselors have difficulty doing to turns interests for the cognitive characteristic and the internal problems by the client, and are using frequency closed-ended question to confirm the interpretation created in ones mind for the client. Therefore, there is the opportunity for education and training which called the supervision to improve the counseling skill of beginner counselor by expert counselors. However, these documents of the verbatim record in the counseling used in the supervision are large-scale and complex, the expert counselors are very difficult to extract the characteristics and situation of the conversation. As appropriate method to visualize each reaction of the client for each question by beginner counselor, 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 document is very low in the current system. To improve this problem, we have implemented on the category classification method for text data of conversation document with SVM (Support Vector Machine) as machine learning technique. In addition, we have compared and evaluated with the result of the initial classification in the current system. As these results, we have shown that the accuracy rate of the classification method with SVM become higher than the result in the current system.
KeywordsPsychological counseling Machine learning Text classification
We are deeply grateful to the example presenters and clients who had you willingly consent about this example data offer. Special thanks also to the Japan Yoga Therapy Society, for having study support go generously.
- 1.Rogers, C.R.: Client-centered therapy: its current practice, implications and theory. Houghton Mifflin College Div (1951)Google Scholar
- 2.Uetsuji, T., Imai, S., Onoue, Y., Kamata, M., Ebara, Y., Koyamada, K.: Construction on visualization system of flow of conversation in counseling. In: Proceedings of International Conference on Simulation Technology (JSST2016), pp. 364–369 (2016)Google Scholar
- 3.Ansbacher H. L., Ansbacher R.: The Individual Psychology of Alfred Adler, Haper Row Publishers Inc, New York (1956)Google Scholar
- 4.Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers, the fifth annual workshop on Computational learning theory, pp. 144–152 (1992)Google Scholar
- 5.Taira, H., Mukouchi, T., Haruno, M.: Text categorization using suport vector machie (in Japanese), IPSJ Technical Report, 1998-NL-128 (1998)Google Scholar
- 6.Mikolov, T., Yih, W., Zweig, G.: Linguistic regularities in continuous space word representations, NACCL13, pp. 746–751 (2013)Google Scholar
- 7.Nozawa, K., Nakaoka, Y., Yamamoto, S., Satoh, T.: Finding method of replaceable ingredients using large mounts of cooking recipes (in Japanese), The Institute of Electronics, Information and Communication Engineers, Technical Report, 114(204), pp. 41–46 (2014)Google Scholar
- 8.Sugawara, T., Takamura, H., Sasano, R., Okumura, M.: Context Representation with Word Embeddings for WSD, Computational Linguistics. Springer, New York pp. 108–119 (2015)Google Scholar
- 9.Xing, C., Wang, D., Zhang, X., Liu, C.: Document classification with distributions of word vectors. In: 2014 Annual Summit and Conference on Asia-Pacific Signal and Information Processing Association (2014)Google Scholar
- 10.Yahoo! Chebukuro. http://chiebukuro.yahoo.co.jp/
- 11.MeCab: Yet another part-of-speech and morphological analyzer. http://taku910.github.io/mecab/
- 12.Scikit-learn machine learning in python. http://scikit-learn.org/stable/