Qualitative verbal and text data such as narrations and descriptions can include additional information about a subject’s spiritual response or deep psyche than questionnaires as quantitative data. Therefore, in the previous studies, various analysis methods have been developed to clarify and visualize the subject’s mental state based on these data. The present study focuses on the sequential transitions of the visualization results from our previous study. The purpose of this study was to reveal and visualize the transition patterns of the subjects’ mental states by analyzing their utterances. The mental changes were expressed with trajectories in two-dimensional space in which the relationship between various emotions was represented with self-organizing maps (SOM) as analyzed in the previous study. The present study demonstrated the modal patterns of mental changes among the subject groups by clustering the subjects. These patterns were visualized on the two-dimensional space composed of psychological evaluation axes, thus the visualization results were interpreted in terms of psychology. It was concluded that the group’s tendencies in terms of mental changes can be comprehended by noting on the transition of the mental states.
References
[1]
Aoki, K., Hirahara, H., Kato, C., & Tsucida, K. (2018). A Consideration on Emotional Topology: Verbal Data Processing and Representation Applied to Athlete Statements. Psychology, 9, 876-895. https://doi.org/10.4236/psych.2018.94054
[2]
Bajpai, R., Poria, S., Ho, D., & Cambria, E. (2017). Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish. CoRR, abs/1707.04408.
[3]
Cavicchiolo, E., Alivernini, F., & Manganelli, S. (2015). A Mixed Method Study on Teachers’ Diaries: Teachers’ Narratives and Value-added Patterns. Procedia-Social and Behavioral Sciences, 205, 485-492. https://doi.org/10.1016/j.sbspro.2015.09.048
[4]
Cohn, M. A., Mehl, M. R., & Pennebaker, J. W. (2004). Linguistic Markers of Psychological Change Surrounding September 11, 2001. Psychological Science, 15, 687-693. https://doi.org/10.1111/j.0956-7976.2004.00741.x
[5]
Digman, J. M. (1990). Personality Structure: Emergence of the Five-Factor Model. Annual Review of Psychology, 41, 417-440. https://doi.org/10.1146/annurev.ps.41.020190.002221
[6]
Gottschalk, L. A., & Gleser, G. C. (1969). The Measurement of Psychological States through the Content Analysis of Verbal Behavior. Berkeley: University of California Press.
[7]
Inami, M., Saito, M., Horii, K., Hosoi, H., Yamagata, T., Fujisawa, N. et al. (2013). Visualization of “The Tale of Genji”. Transactions of Visualization Society of Japan, 33, 97-107. https://doi.org/10.3154/jvs.33.11
[8]
Irving, J. A., Park-Saltzman, J., Fitzpatrick, M., Dobkin, P. L., Chen, A., & Hutchinson, T. (2014). Experiences of Health Care Professionals Enrolled in Mindfulness-Based Medical Practice: A Grounded Theory Model. Mindfulness, 5, 60-71. https://doi.org/10.1007/s12671-012-0147-9
[9]
Kanaya, S., Kinouchi, M., Abe, T., Kudo, Y., Yamada, Y., Nishi, T. et al. (2001). Analysis of Codon Usage Diversity for Bacterial Genes with a Self-Organizing Map (SOM): Characterization of Horizontally Transferred Genes with Emphasis on the E. coli O157 Genome. Gene, 276, 89-99. https://doi.org/10.1016/S0378-1119(01)00673-4
[10]
Kohonen, T. (1995). Self-Organizing Maps (Vol. 30). Springer Series in Information Sciences, Berlin, Heidelberg, New York: Springer.
[11]
Lam, S., & Lee, D. L. (1999). Feature Reduction for Neural Network Based Text Categorization. 6th International Conference on Database Systems for Advanced Applications, 195-202.
[12]
Mackinlay, J. D. (2000). Opportunities for Information Visualization. IEEE Computer Graphics and Applications, 20, 22-23. https://doi.org/10.1109/38.814540
[13]
McCrae, R. R., & Costa, P. T. (1987). Validation of the Five-Factor Model of Personality across Instruments and Observers. Journal of Personality and Social Psychology, 52, 81-90. https://doi.org/10.1037/0022-3514.52.1.81
[14]
Mishne, G., & de Rijke, M. (2006). MoodViews: Tools for Blog Mood Analysis. AAAI Spring Symposium-Technical Report, 153-154.
[15]
Miura, A., Komori, M., Matsumura, N., & Maeda, K. (2015). Expression of Negative Emotional Responses to the 2011 Great East Japan Earthquake: Analysis of Big Data from Social Media. The Japanese Journal of Psychology, 86, 102-111. https://doi.org/10.4992/jjpsy.86.13076
[16]
Ni, X., He, P., Xu, W., Gong, Y., Zhu, Q., Huang, W., & Wang, J. (2017). Research on Cigarettes Customer Needs Importance Algorithm Based on KJ/RAHP/KANO (p. 139). MATEC Web of Conferences.
[17]
Rosenberg, S. D., & Tucker, G. J. (1978). Verbal Behavior and Schizophrenia: The Semantic Dimension. Archives of General Psychiatry, 36, 1331-1337. https://doi.org/10.1001/archpsyc.1979.01780120061008
[18]
Seale, C., Ziebland, S., & Charteris-Black, J. (2006). Gender, Cancer Experience and Internet Use: A Comparative Keyword Analysis of Interviews and Online Cancer Support Groups. Social Science & Medicine, 62, 2577-2590. https://doi.org/10.1016/j.socscimed.2005.11.016
[19]
Shinkai, K. (2008). Fuzzy Cluster Analysis and Its Evaluation Method. Biomedical Fuzzy and Human Sciences, 13, 3-9.
[20]
Stiles, W. B. (1992). Describing Talk: A Taxonomy of Verbal Response Modes. Newbury Park, CA: Sage.
[21]
Yatsuzuka, I. (2007). Newspaper Article Analysis on the Social Construction Process of “Volunteer” and “NPO” in Japan: An Attempt of “Postpositional Particle Analysis”. The Japanese Journal of Educational & Social Psychology, 46, 103-119. https://doi.org/10.2130/jjesp.46.103