Identifying Psychological Theme Words from Emotion Annotated Interviews
Recent achievements in Natural Language Processing (NLP) and Psychology invoke the challenges to identify the insight of emotions. In the present study, we have identified different psychology related theme words while analyzing emotions on the interview data of ISEAR (International Survey of Emotion Antecedents and Reactions) research group. Primarily, we have developed a Graphical User Interface (GUI) to generate visual graphs for analyzing the impact of emotions with respect to different background, behavioral and physiological variables available in the ISEAR dataset. We have discussed some of the interesting results as observed from the generated visual graphs. On the other hand, different text clusters are identified from the interview statements by selecting individual as well as different combinations of the variables. Such textual clusters are used not only for retrieving the psychological theme words but also to classify the theme words into their respective emotion classes. In order to retrieve the psychological theme words from the text clusters, we have developed a rule based baseline system considering unigram based keyword spotting technique. The system has been evaluated based on a Top-n ranking strategy (where n=10, 20 or 30 most frequent theme words). Overall, the system achieves the average F-Scores of .42, .32, .36, .42, .35, .40 and .40 in identifying theme words with respect to Joy, Anger, Disgust, Fear, Guilt, Sadness and Shame emotion classes, respectively.
KeywordsTheme Word Psychology Emotions Symptoms Interview
Unable to display preview. Download preview PDF.
- 1.Quirk, R., Greenbaum, S., Leech, G., Svartvik, J.: A Comprehensive Grammar of the English Language. Longman, New York (1985)Google Scholar
- 2.Grefenstette, G., Qu, Y., Shanahan, J.G., Evans, D.A.: Coupling niche browsers and affect analysis for an opinion mining application. In: Proceedings of RIAO 2004, pp. 186–194 (2004)Google Scholar
- 4.Strapparava, C., Mihalcea, R.: Learning to Identify Emotions in Text (2008)Google Scholar
- 5.Stone, P.J.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press (1966)Google Scholar
- 6.Weintraub, W.: Verbal behavior: Adaptation and psychopathology. Springer, New York (1981)Google Scholar
- 8.Das, D., Bandyopadhyay, S.: Analyzing Emotional Statements – Roles of General and Physiological Variables. In: The Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP), 5th International Conference on Natural Language Processing (IJCNLP 2011), Chiang Mai, Thailand, pp. 59–67 (2011)Google Scholar
- 10.Das, D.: Analysis and Tracking of Emotions in English and Bengali Texts: A Computational Approach. In: The Proceedings of the Ph. D. Symposium, 20th International World Wide Web Conference (WWW 2011), Hyderabad, India, pp. 343–348 (2011)Google Scholar