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Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels

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Abstract

A good novel can often elicit from a reader strong sentiments similar to the moods, feelings, and attitudes depicted in the novel. With the rapid progress in AI, sentiment-based arcs in novels can now be reliably extracted and used to summarize the novel’s plot in the story arc. Are there salient mathematical properties that underlie such story arcs and have far-reaching implications in the writing, adaptation, and reading of the novel? To gain insights into this question, we employ multifractal theory to characterize the narrative coherence and dynamic evolution of sentiments of the novel, Never Let Me Go, by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an example. Three methods are compared for fractal scaling analysis, the classic variance-time method, an improvement of the variance-time relation based on adaptive filtering, and adaptive fractal analysis. We find that while variance-time relation fails to accurately extract the fractal scaling exponent, adaptive fractal analysis succeeds in fully characterizing the fractal variations in the sentiment dynamics. The finding may be indicative of the potential that multifractal theory has for computational narratology and large-scale literary analysis, especially for inferring the degree of narrative coherence and variation of the plot of a novel.

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References

  1. Agarwal, A., Biadsy, F., Mckeown, K.R.: Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 24–32 (2009)

  2. Aslam, F., Awan, T.M., Syed, J.H., et al.: Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications, 7(1), 1–9 (2020)

  3. Bortolussi, M., Dixon, P.: Psychonarratology: Foundations for the Empirical Study of Literary Response. Cambridge University Press, London (2002)

  4. Bowers, M., Gao, J.B., Tung., W.W.: Long-Range Correlations in Tree Ring Chronologies of the USA: Variation Within and Across Species. Geophysical Research Letters. 40(3), 568–572 (2013)

  5. Brooke, J., et al.: GutenTag: An NLP-driven Tool for Digital Humanities Research in the Project Gutenberg Corpus. In: Proceedings of NAACL-HLT Fourth Workshop on Computational Linguistics for Literature, 42–47 (2015)

  6. Cambria, E.: Affective computing and sentiment analysis. IEEE Intelligent Systems. 31(2), 102–107 (2016)

  7. Cambria, E., Schuller, B., Xia, Y.Q., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems. 28(2), 15–21 (2013)

  8. Carroll, R.: Imitations of life: cloning, heterosexuality and the human in Kazuo Ishiguro’s Never let me go. Journal of gender studies. 19(1), 59–71 (2010)

  9. Chen, Z., Hu, K., Carpena, P., et al.: Effect of nonlinear filters on detrended fluctuation analysis. Physical Review E. 71(1), 011104 (2005)

  10. Choudhury, P., Wang, D., Carlson, N.A., et al.: Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles. Strategic Management Journal, 40(11), 1705–1732 (2019)

  11. Collins, J.J., De Luca C.J.: Random walking during quiet standing. Physical review letters. 73(5), 764 (1994)

  12. Do, H.H., Prasad, P.W.C., Maag, A., et al.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Systems with Applications, 118, 272–299 (2019)

  13. Dodds, P.S., Clark, E.M., Desu, S., et al.: Human language reveals a universal positivity bias. Proceedings of the National Academy of Sciences. 112(8), 2389–2394 (2015)

  14. Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 69–78 (2014)

  15. Furstenau, N.: A nonlinear dynamics model for simulating long range correlations of cognitive bistability. Biological Cybernetics. 103(3), 175–198 (2010)

  16. Gao, J.B.: Analysis of Amplitude and Frequency Variations of Essential and Parkinsonian Tremors. Medical and Biological Engineering and Computing. 42(3), 345–349 (2004)

  17. Gao, J.B., Billock, V.A., Merk, I., et al.: Inertia and memory in ambiguous visual perception. Cognitive Processing. 7(2), 105–112 (2006)

  18. Gao, J.B., Cao, Y., Lee, J.M.: Principal component analysis of 1/f noise. Physics Letters A. 314(5–6), 392–400 (2003)

  19. Gao, J.B., Cao, Y.H., Tung, W.W., Hu, J.: Multiscale Analysis of Complex Time Series, Integration of Chaos and Random Fractal Theory and Beyond. John Wiley & Sons, New Jersey (2007)

  20. Gao, J.B., Fang, P., Liu, F.Y.: Empirical scaling law connecting persistence and severity of global terrorism. Physica A. 482, 74–86 (2017)

  21. Gao, J.B., Hu, J., Buckley, T., White, K., Hass, C.: Shannon and Renyi Entropies To Classify Effects of Mild Traumatic Brain Injury on Postural Sway. PLoS ONE. 6(9), e24446 (2011)

  22. Gao, J.B., Hu, J., Mao, X., Perc, M.: Culturomics meets random fractal theory: Insights into long-range correlations of social and natural phenomena over the past two centuries. J. Royal Society Interface. 9(73), 1956–1964 (2012)

  23. Gao, J., Hu, J., Tung, W.W.: Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering. PLoS ONE. 6(9), e24331 (2011)

  24. Gao, J.B., Hu, J., Tung, W.W.: Complexity measures of brain wave dynamics. Cognitive Neurodynamics. 5(2), 171–182 (2011)

  25. Gao, J.B., Hu, J., Tung, W.W., Zheng, Y.: Multiscale analysis of economic time series by scale-dependent Lyapunov exponent. Quantitative Finance. 13(2), 265–274 (2013)

  26. Gao, J., Jockers, M.L., Laudun, J., et al.: A multiscale theory for the dynamical evolution of sentiment in novels. In: 2016 International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 1–4 (2016)

  27. Gao, J., Qi, Y., Cao, Y., Tung, W.-W.: Protein coding sequence identification by simultaneously characterizing the periodic and random features of DNA sequences. BioMed Research International. 2005(2), 139–146 (2005)

  28. Gao, J.B., Sult, H., Hu, J., Tung, W.W.: Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. IEEE Signal Processing Letters. 17(3), 237–240 (2010)

  29. Gilden, D.L., Thornton, T., Mallon, M.W.: 1/f noise in human cognition. Science. 267(5205), 1837–1839 (1995)

  30. Hasan, A., Moin, S., Karim, A., et al.: Machine learning-based sentiment analysis for twitter accounts. Mathematical and Computational Applications, 23(1), 11 (2018)

  31. Hogan P.C.: Affective Narratology: The Emotional Structure of Stories. University of Nebraska Press, Nebraska (2011)

  32. Hu, J., Gao, J.B., Cao, Y.H., Bottinger, E., Zhang, W.J.: Exploiting noise in array CGH data to improve detection of DNA copy number change. Nucleic acids research. 35(5), e35 (2007)

  33. Hu, J., Gao, J., Wang, X.: Multifractal analysis of sunspot time series: the effects of the 11-year cycle and Fourier truncation. Journal of Statistical Mechanics: Theory and Experiment. 2009(02), P02066 (2009)

  34. Hu, K., Ivanov, P.C., Chen, Z., et al.: Effect of trends on detrended fluctuation analysis. Physical Review E. 64(1), 011114 (2001)

  35. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 168–177 (2004)

  36. Hu, J., Zheng, Y., Gao, J.B.: Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements. Frontiers in Neurology. 4, 158–158 (2013)

  37. Ishiguro, K.: Never Let Me Go. Faber & Faber, London (2005)

  38. Ivanov, P.C., Rosenblum, M.G., Amaral, L.A.N., Struzik, Z.R., Havlin, S., Goldberger, A.L., Stanley, H.E.: Multifractality in human heartbeat dynamics. Nature. 399(6735), 461–465 (1999)

  39. Ivanov, P.C., Rosenblum, M.G., Peng, C.K., Mietus, J., Havlin, S., Stanley, H.E., Goldberger, A.Ł.: Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis. Nature. 383(6598), 323–327 (1996)

  40. Jockers, M.L.: Syuzhet: Extracts Sentiment and Sentiment-Derived Plot Arcs from Text, [online] Available: https://cran.r-project.org/web/packages/syuzhet/index.html (2016). Accessed 10 May (2019)

  41. Jockers, M.: Revisiting Chapter Nine of Macroanalysis. [online] Available: http://www.matthewjockers.net/ (2019). Accessed 10 May (2019)

  42. Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., et al.: Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications. 316(1-4), 87–114 (2002)

  43. Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics, 1367 (2004)

  44. Leland, W.E., Taqqu, M.S., Willinger, W., Wilson, D.V.: On the self-similar nature of Ethernet traffic (extended version). IEEE/ACM Trans. Networking. 2(1), 1–15 (1994)

  45. Levy, T.: Human Rights Storytelling and Trauma Narrative in Kazuo Ishiguro’s Never Let Me Go. Journal of Human Rights. 10(1), 1–16 (2011)

  46. Mandelbrot, B.B.: The fractal geometry of nature. WH freeman, New York (1983)

  47. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal. 5(4), 1093–1113 (2014)

  48. Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 2004 conference on empirical methods in natural language processing, 412–418 (2004)

  49. Nielbo, K.L., Baunvig, K.F., Liu, B., et al.: A curious case of entropic decay: Persistent complexity in textual cultural heritage[J]. Digital Scholarship in the Humanities, 34(3): 542–557 (2019)

  50. Nielsen, F.A.: A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC Workshop on ‘Making Sense of Microposts’: Big things come in small packages, 93–98 (2011)

  51. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, 271–278 (2004)

  52. Peng, C.K., Buldyrev, S.V., Goldberger, A.L., et al.: Long-range correlations in nucleotide sequences. Nature. 356(6365), 168–170 (1992)

  53. Peng, C.K., Buldyrev, S.V., Havlin, S., et al.: Mosaic organization of DNA nucleotide. Physical review E. 49(2), 1685 (1994)

  54. Reagan, A.J., Mitchell, L., Kiley, D., et al.: The emotional arcs of stories are dominated by six basic shapes. EPJ Data Science. 5(1), 31 (2016)

  55. Reagan, A.J., et al.: Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs. EPJ Data Science, 6:1:21 (2017)

  56. Thomsen, M.R.: The New Human in Literature: Posthuman Visions of Changes in Body, Mind and Society after 1900. A & C Black, London (2013)

  57. Tung, W., Gao, J., Hu, J., et al.: Detecting chaos in heavy-noise environments. Physical Review E. 83(4), 046210 (2011)

  58. Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 417–424 (2002)

  59. Voss, R.F.: Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. Physical review letters. 68(25), 3805 (1992)

  60. Wevers, M., Gao, J., Nielbo, K.L.: Tracking the Consumption Junction: Temporal Dependencies between Articles and Advertisements in Dutch Newspapers, Digital Humanities Quarterly 014 (2020)

  61. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, 347–354 (2005)

  62. Wolf, M.: 1/f noise in the distribution of prime numbers. Physica A. 241, 493 (1997)

  63. Yadav, A., Vishwakarma, D.K.: Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6), 4335–4385 (2020)

  64. Yanqing, C., Ding, M., Kelso, J.A.S.: Long memory processes (1/f type) in human coordination. Physical Review Letters. 79(22), 4501 (1997)

  65. Zhang, L., Liu, B.: Sentiment Analysis and Opinion Mining, Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA, 1152–1161 (2017)

  66. Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), 1–46 (2018)

  67. Zheng, Y., Gao, J.B., Sanchez, J.C., Principe, J.C., Okun, M.S.: Multiplicative multifractal modeling and discrimination of human neuronal activity. Phys. Lett. A. 344(2–4), 253–264 (2005)

  68. Zhu, H.B.: Fractal behavior in the headway fluctuation simulated by the NaSch model. Physica A. 398, 187–193 (2014)

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Acknowledgments

This research was supported by the National Natural Science Foundation of China under Grant Nos. 71661002 and 41671532 and by the Fundamental Research Funds for the Central Universities. It is also supported by the National Key Research and Development Program of China, under grant number 2019AAA0103402. One of the authors (JG) also benefited tremendously from participating the long program on culture analytics organized by the Institute for Pure and Applied Mathematics (IPAM) at UCLA, which was supported by the National Science Foundation.

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Authors and Affiliations

  1. Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Qiyue Hu & Jianbo Gao

  2. Business School, Guangxi University, Nanning, China

    Bin Liu

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China and International College, Guangxi University, Nanning, China

    Jianbo Gao

  4. Center for Humanities Computing, Aarhus University, Aarhus, Denmark

    Kristoffer L. Nielbo

  5. School of Communication and Culture, Aarhus University, Aarhus, Denmark

    Mads Rosendahl Thomsen

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  1. Qiyue Hu
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  2. Bin Liu
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  3. Jianbo Gao
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  4. Kristoffer L. Nielbo
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  5. Mads Rosendahl Thomsen
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Contributions

J.G., Kristoffer and B.L. conceived the study. J.G., Kristoffer, Mads, B.L. and Q.Y. designed and performed the research. Q.Y., B.L. and J.G. analyzed data. J.G., Kristoffer, Mads, B.L. and Q.Y. drafted the manuscript. All authors reviewed the manuscript.

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Correspondence to Jianbo Gao or Kristoffer L. Nielbo.

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This is an extended version of the BESC 2019 conference paper, adding more and deeper analysis. The original title is Dynamic evolution of sentiments in Never Let Me Go : insights from quantitative analysis and implications.

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Hu, Q., Liu, B., Gao, J. et al. Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels. World Wide Web 24, 1147–1164 (2021). https://doi.org/10.1007/s11280-021-00892-5

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