- Volume 18 Issue 9
DOI QR Code
Detection of Character Emotional Type Based on Classification of Emotional Words at Story
스토리기반 저작물에서 감정어 분류에 기반한 등장인물의 감정 성향 판단
- Baek, Yeong Tae (Dept. of Multimedia, Kimpo College)
- 백영태 (김포대학교 멀티미디어과)
- Received : 2013.08.19
- Accepted : 2013.09.03
- Published : 2013.09.30
In this paper, I propose and evaluate the method that classifies emotional type of characters with their emotional words. Emotional types are classified as three types such as positive, negative and neutral. They are selected by classification of emotional words that characters speak. I propose the method to extract emotional words based on WordNet, and to represent as emotional vector. WordNet is thesaurus of network structure connected by hypernym, hyponym, synonym, antonym, and so on. Emotion word is extracted by calculating its emotional distance to each emotional category. The number of emotional category is 30. Therefore, emotional vector has 30 levels. When all emotional vectors of some character are accumulated, her/his emotion of a movie can be represented as a emotional vector. Also, thirty emotional categories can be classified as three elements of positive, negative, and neutral. As a result, emotion of some character can be represented by values of three elements. The proposed method was evaluated for 12 characters of four movies. Result of evaluation showed the accuracy of 75%.
- K.-E. Ko, and K.-B. Sim, "Development of Context Awareness Service Inference Method using Multi-Modal Emotion Recognition System," Autumn Conference on Korea Institute of Intelligent System, Vol. 18, No. 2, pp. 261-264, Oct. 2008.
- S.-B. Park, E. You, and J.J. Jung, "Potential Emotion Word in Movie Dialog," Proceedings of the International Conference on IT Convergence and Security 2011, pp. 507-516, Dec. 2011.
- Yassine, M. and Hajj, H., "A Framework for Emotion Mining from Text in Online Social Networks," Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, pp. 1136-1142, 2010.
- Strapparava, C. and Valitutti, A., "WordNet-Affect: an Affective Extension of WordNet," In Proceedings of the 4th International Conference on Language Resources and Evaluation, pp. 1083-1086, 2004.
- Esuli, A. and Sebastiani, F., "SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining," In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC'06), pp. 417-422, 2006.
- Elliot, C., "The Affective Reasoner: A Process Model of Emotions in a Multi-agent System," PhD thesis, Northwestern University, May 1992. The Institute for the Learning Sciences, Technical Report No. 32.
- Salway, A., Graham, M., "Extracting Information about Emotions in Films," In Proceedings of the eleventh ACM international conference on Multimedia (MULTIMEDIA '03), pp. 299-302, 2003.
- Liu, H., Lieberman, H. and Selker, T., "A Model of Textual Affect Sensing Using Real-World Knowledge," In Proceedings of the 2003 International Conference on Intelligent User Interfaces, pp. 125-132, 2003.
- C. Y. Weng, W. T. Chu, and J. L. Wu, "RoleNet: movie analysis from the perspective of social network," IEEE Transaction on Multimedia, vol. 11, no. 2. pp. 256-271, 2009. https://doi.org/10.1109/TMM.2008.2009684
- S.-B. Park, K.-J. Oh, and G.-S. Jo, "Social Network Analysis in a Movie using Character-net," Multimedia Tools and Applications. Vol. 59, No. 2, pp. 601-627, 2012. 7. https://doi.org/10.1007/s11042-011-0725-1
- J. Kaminski, and M. Schober, "Social networks in movies," COINs Conference, pp. 1-3, 2011.
- S.-B. Park and G.-S. Jo, "Role Grades Classification and Community Clustering at Character-net," Journal of the Korea Society of Computer and Information, vol. 14, n. 11, pp. 169-178, Nov. 2009.
- W. Kim, S.-B. Park, G.-S. Jo, "Improvement of Character-net via Detection of Conversation Participant," Journal of the Korea Society of Computer and Information, vol. 14, n. 10, pp. 241-249, Oct. 2009.
- G.A. Miller, "WordNet: A Lexical Database for English," Communications of the ACM, Vol. 38, No. 11, pp. 39-41, 1995.