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Story visualization of novels with multi-theme keyword density analysis

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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

    Article  Google Scholar 

  • Chen Z (1996) Generating suggestions through document structure mapping. Decis Support Syst 16:297–314

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Ido T, Murai Y (2006) A recursive interpolation algorithm for particle tracking velocimetry. Flow Meas Instrum 17:267–275

    Article  Google Scholar 

  • Kuhn A, Ducasse S et al (2007) Semantic clustering: identifying topics in source code. Inf Softw Tech 49:230–243

    Article  Google Scholar 

  • Lee C, Lee GG et al (2007) Dependency structure language model for topic detection and tracking. Inf Process Manag 43:1249–1259

    Article  Google Scholar 

  • Meng C, Wong K (2004) A GXL schema for story diagrams. Electron Notes Theor Comp Sci 94:29–38

    Article  Google Scholar 

  • Pons-Porrata A, Berlanga-Llavori R et al (2007) Topic discovery based on text mining techniques. Inf Process Manag 43:752–768

    Article  Google Scholar 

  • Segel E, Heer J (2010) Narrative visualization: telling stories with data. IEEE Trans Visual Comput Graph 16(6):1139–1148

    Article  Google Scholar 

  • Shillingsburg PL (2006) From Gutenberg to Google: electronic representation of literary texts. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Singh S, Dey L (2005) A new customized document categorization scheme using rough membership. Appl Soft Comp 5:373–390

    Article  Google Scholar 

  • Yamada M, Murai Y (2009) Story visualization of literary works: how a computer reads Shakespeare’s plays. J Visual 12:181–188

    Article  Google Scholar 

  • Yamada M, Murai Y (2010) Stereoscopic story visualization in literary works demonstrated by Shakespeare’s plays. J Visual 13:355–363

    Article  Google Scholar 

  • Yoon B, Park Y (2005) A systematic approach for identifying technology opportunities: keyword-based morphology analysis. Technol Forecast Soc 72:145–160

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Graduate School of Letters, Hokkaido University, N10W7, Sapporo, 060-0810, Japan

    Miyuki Yamada

  2. Faculty of Engineering, Hokkaido University, N13W8, Sapporo, 060-8628, Japan

    Yuichi Murai

  3. School of Science and Engineering, Meisei University, 2-1-1 Hodokubo, Hino, Tokyo, 191-8506, Japan

    Ichiro Kumagai

Authors
  1. Miyuki Yamada
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  2. Yuichi Murai
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  3. Ichiro Kumagai
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Correspondence to Miyuki Yamada.

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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

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