Abstract
A new method for visualization of stories in literary works was explored. Our story visualization method consists of two parts: keyword-based statistical analysis for multiple themes and imagery expression of the results for visual understanding. In this study, we focused on novels as the targets, and discussed ways in which complex structures can be simultaneously visualized using multiple themes. The method was applied for the comparison of Charles Dickens’ novels with Shakespeare’s plays in order to identify any existing evidence concerning literal interest created by the overlapping of multiple scenarios in a single story. We also applied the method to non-literary documents such as newspaper articles, and showed that these documents contain simple statistic patterns regarding a given theme, which contrasts with the case involving novels that include the dynamic fluctuation of individual story elements.
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References
Chatera N, Vitanyi P (2007) Ideal learning of natural language: positive results about learning from positive evidence. J Math Psychol 51:135–163
Chen Z (1996) Generating suggestions through document structure mapping. Decis Support Syst 16:297–314
Dove G and Jones S (2012) Narrative visualization: sharing insights into complex data. In: Proceedings of the interfaces and human computer interaction (IHCI 2012), 21–23 July 2012, Lisbon, Portugal
Hullman J, Diakopoulos N (2011) Visualization rhetoric: framing effects in narrative visualization. IEEE Trans Visual Comput Graph 17(12):2231–2240
Ido T, Murai Y (2006) A recursive interpolation algorithm for particle tracking velocimetry. Flow Meas Instrum 17:267–275
Kuhn A, Ducasse S et al (2007) Semantic clustering: identifying topics in source code. Inf Softw Tech 49:230–243
Lee C, Lee GG et al (2007) Dependency structure language model for topic detection and tracking. Inf Process Manag 43:1249–1259
Meng C, Wong K (2004) A GXL schema for story diagrams. Electron Notes Theor Comp Sci 94:29–38
Pons-Porrata A, Berlanga-Llavori R et al (2007) Topic discovery based on text mining techniques. Inf Process Manag 43:752–768
Segel E, Heer J (2010) Narrative visualization: telling stories with data. IEEE Trans Visual Comput Graph 16(6):1139–1148
Shillingsburg PL (2006) From Gutenberg to Google: electronic representation of literary texts. Cambridge University Press, Cambridge
Singh S, Dey L (2005) A new customized document categorization scheme using rough membership. Appl Soft Comp 5:373–390
Yamada M, Murai Y (2009) Story visualization of literary works: how a computer reads Shakespeare’s plays. J Visual 12:181–188
Yamada M, Murai Y (2010) Stereoscopic story visualization in literary works demonstrated by Shakespeare’s plays. J Visual 13:355–363
Yoon B, Park Y (2005) A systematic approach for identifying technology opportunities: keyword-based morphology analysis. Technol Forecast Soc 72:145–160
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Yamada, M., Murai, Y. & Kumagai, I. Story visualization of novels with multi-theme keyword density analysis. J Vis 16, 247–257 (2013). https://doi.org/10.1007/s12650-013-0163-4
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DOI: https://doi.org/10.1007/s12650-013-0163-4