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http://dx.doi.org/10.5392/IJoC.2020.16.3.051

A Computational Model to Detect Affective Response Based on Narrative Agent's Knowledge  

Kwon, Hochang (Department of cinema studies, Korea National University of Arts (K'ARTS))
Kwon, Hyuk Tae (Department of Industrial&Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST))
Publication Information
International Journal of Contents / v.16, no.3, 2020 , pp. 51-65 More about this Journal
Abstract
Narratives arouse diverse and rich affective responses to recipients, and this is one of the reasons why narratives are universal and popular. Computational studies on narratives have established a formal model or system of the affective response based on the theory in psychology or media research, and have analyzed or generated a narrative that can evoke a specific affective response. In this paper, we propose a new computational model that can detect the affective response expected to appear in the narrative based on the narrative agent's knowledge. First, we designed a narrative representation model that can elaborately express the event structure and the agent's knowledge as well. Additionally, an analysis method was proposed to detect the three affective responses and the related situational information. Then, we validated the model through a case study about an actual movie narrative. Through the case study, we confirmed that the model captures the affective responses of the audience. The proposed model can be effectively used for the narrative analysis and the creation that must consider the affective responses of the recipient.
Keywords
Narrative Representation; Narrative Analysis; Narrative Agent' Knowledge; Affective Response; Structural Affect Theory;
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1 P. Gervas, B. Lonneker-Rodman, J. C. Meister, and F. Peinado, "Narrative Models: Narratology Meets Artificial Intelligence," in International Conference on Language Resources and Evaluation. Satellite Workshop: Toward Computational Models of Literary Analysis, pp. 44-51, 2006.
2 A. C. Graesser and K. Wiemer-Hastings, "Situation models and concepts in story comprehension," Narrat. comprehension, causality, coherence Essays Honor Tom Trabasso, pp. 77-92, 1999, doi: 10.4324/9781410603135.
3 E. Branigan, Narrative comprehension and film, Routledge, 2013, doi: 10.4324/9781315003108.
4 L. F. Alwitt, "Suspense and advertising responses," J. Consum. Psychol., vol. 12, no. 1, pp. 35-49, 2002, doi: 10.1207/153276602753338135.   DOI
5 Y. G. Cheong and R. M. Young, "Suspenser: A story generation system for suspense," IEEE Trans. Comput. Intell. AI Games, vol. 7, no. 1, pp. 39-52, 2015, doi: 10.1109/TCIAIG.2014.2323894.   DOI
6 B. O'Neill and M. Riedl, "Dramatis: A Computational Model of Suspense," in AAAI, pp. 944-950, 2014.
7 R. J. Gerrig and A. B. I. Bernardo, "Readers as problem-solvers in the experience of suspense," Poetics, vol. 22, no. 6, pp. 459-472, 1994, doi: 10.1016/0304-422x(94)90021-3.   DOI
8 R. A. Zwaan, J. P. Magliano, and A. C. Graesser, "Dimensions of Situation Model Construction in Narrative Comprehension," J. Exp. Psychol. Learn. Mem. Cogn., vol. 21, no. 2, pp. 386-397, 1995, doi: 10.1037/0278-7393.21.2.386.   DOI
9 B. C. Bae and R. M. Young, "A use of flashback and foreshadowing for surprise arousal in narrative using a planbased approach," in Joint International Conference on Interactive Digital Storytelling, pp. 156-167, 2008, doi: 10.1007/978-3-540-89454-4_22.
10 B. C. Bae and R. M. Young, "Suspense? Surprise! or how to generate stories with surprise endings by exploiting the disparity of knowledge between a story's reader and its characters," in Joint International Conference on Interactive Digital Storytelling, pp. 304-307, 2009, doi: 10.1007/978-3-642-10643-9_35.
11 W. F. Brewer and E. H. Lichtenstein, "Stories are to entertain: A structural-affect theory of stories," J. Pragmat., vol. 6, no. 5-6, pp. 473-486, 1982, doi: 10.1016/0378-2166(82)90021-2.   DOI
12 W. F. Brewer and K. Ohtsuka, "Story structure, characterization, just world organization, and reader affect in American and Hungarian short stories," Poetics, vol. 17, no. 4-5, pp. 395-415, 1988, doi: 10.1016/0304-422X(88)90043-5.   DOI
13 H. Kwon, S. Jung, H. T. Kwon, and W. C. Yoon, "A Knowledge Distribution Model to Support an Author in Narrative Creation," in Human Interface and the Management of Information, Information and Knowledge in Applications and Services, Springer, pp. 511-522, 2014, doi: 10.1007/978-3-319-07863-2_49.
14 H. Kwon, H. T. Kwon, and W. C. Yoon, "An information-theoretic evaluation of narrative complexity for interactive writing support," Expert Syst. Appl., vol. 53, pp. 219-230, 2016, doi: 10.1016/j.eswa.2016.01.019.   DOI
15 D. Herman, J. Manfred, and R. Marie-Laure, Routledge encyclopedia of narrative theory, Routledge, 2010, doi: 10.4324/9780203932896.
16 M. C. Green, T. C. Brock, and G. F. Kaufman, "Understanding media enjoyment: The role of transportation into narrative worlds," Commun. Theory, vol. 14, no. 4, pp. 311-327, 2004, doi: 10.1111/j.1468-2885.2004.tb00317.x.   DOI
17 G. Genette, Narrative discourse revisited, Cornell University Press, 1988, doi: 10.2307/3730539.
18 A. C. Graesser, K. K. Millis, and R. A. Zwaan, "Discourse comprehension," Annu. Rev. Psychol., vol. 48, no. 1, pp. 163-189, 1997, doi: 10.1146/annurev.psych.48.1.163.   DOI
19 J. P. Van Overschelde, "Metacognition: Knowing about knowing," Handb. Metamemory Mem., pp. 47-71, 2013, doi: 10.4324/9780203805503.
20 P. W. Thorndyke, "Cognitive structures in comprehension and memory of narrative discourse," Cogn. Psychol., vol. 9, no. 1, pp. 77-110, 1977, doi: 10.1016/0010-0285(77)90005-6.   DOI