The Limits of Artificial Intelligence in the Analysis of the English Novel
DOI:
https://doi.org/10.25130/jfa.conf.10.3.24Keywords:
Artificial Intelligence, English Novel, Literary Analysis, Narrative Ambiguity, Sentiment Analysis, Cultural ContextAbstract
This study explores the limitations of artificial intelligence (AI) in analyzing the English novel, highlighting both technical and philosophical challenges. While AI tools such as natural language processing models, sentiment analyzers, and transformer-based generators offer new possibilities in literary studies, they fall short in areas that require interpretive sensitivity, cultural awareness, and emotional depth. The paper examines the reasons behind AI’s inability to process narrative ambiguity, irony, metaphor, all of which are essential to understanding literary texts. Through critical analysis and theoretical reflection, this research argues that literature’s richness lies in its ambiguity, affective complexity, and cultural embeddedness, features that AI cannot meaningfully interpret.
Downloads
References
Abbott, H. P. (2008). The Cambridge introduction to narrative (2nd ed.). Cambridge University Press.
Ashcroft, B., Griffiths, G., & Tiffin, H. (2002). The empire writes back: Theory and practice in post-colonial literatures (2nd ed.). Routledge.
Attardo, S. (2000). Irony markers and functions: Towards a goal-oriented theory of irony and its processing. Rask, 12, 3–20.
Bakhtin, M. M. (1981). The dialogic imagination: Four essays (M. Holquist, Ed., C. Emerson & M. Holquist, Trans.). University of Texas Press.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
Boden, M. A. (2004). The creative mind: Myths and mechanisms (2nd ed.). Routledge.
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
Booth, W. C. (1983). The rhetoric of fiction (2nd ed.). University of Chicago Press.
Branigan, E. (1992). Narrative comprehension and film. Routledge.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Burrows, J. (2004). Textual analysis. In S. Schreibman, R. Siemens, & J. Unsworth (Eds.), A companion to digital humanities (pp. 323–347). Blackwell.
Cohn, D. (1978). Transparent minds: Narrative modes for presenting consciousness in fiction. Princeton University Press.
Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier? In Proceedings of the 20th European Conference on Artificial Intelligence (pp. 21–26). IOS Press.
Culler, J. (1997). Literary theory: A very short introduction. Oxford University Press.
Drucker, J. (2020). Visualization and interpretation: Humanistic approaches to display. MIT Press.
Elliott, K. (2019). The strange world of algorithms in literary studies. Stanford University Press.
Felski, R. (2008). Uses of literature. Blackwell.
Fish, S. (1980). Is there a text in this class? The authority of interpretive communities. Harvard University Press.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
Forster, E. M. (1927). Aspects of the novel. Harcourt, Brace and Company.
Gadamer, H. G. (2004). Truth and method (2nd ed., J. Weinsheimer & D. G. Marshall, Trans.). Continuum.
Gibbs, R. W. (1994). The poetics of mind: Figurative thought, language, and understanding. Cambridge University Press.
Goodman, N. (1976). Languages of art: An approach to a theory of symbols (2nd ed.). Hackett Publishing.
Hoover, D. L. (2017). Language and style in computational literary studies. In M. Burke (Ed.), The Routledge handbook of stylistics (pp. 471–486). Routledge.
Ingle, S. (1993). George Orwell: A political life. Manchester University Press.
Iser, W. (1978). The act of reading: A theory of aesthetic response. Johns Hopkins University Press.
Jockers, M. L. (2013). Macroanalysis: Digital methods and literary history. University of Illinois Press.
Johnson, C. L. (2014). Jane Austen: Women, politics, and the novel. University of Chicago Press.
Kellogg, D. (2018). Narrative and the making of US national security. Cambridge University Press.
Kermode, F. (1979). The genesis of secrecy: On the interpretation of narrative. Harvard University Press.
Kim, E., & Klinger, R. (2019). Frowning Frodo, wincing Leia, and a seriously sarcastic Harry: Learning implicit emotion expressions in movie subtitles. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3724–3735.
Lanser, S. S. (1981). The narrative act: Point of view in prose fiction. Princeton University Press.
Ledger, S., & Luckhurst, R. (2000). The fin de siècle: A reader in cultural history, c. 1880–1900. Oxford University Press.
Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon Books.
Maynard, D., & Greenwood, M. A. (2014). Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. In LREC 2014 Proceedings (pp. 4238–4243).
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919.
McCormack, J., & d’Inverno, M. (2012). On the future of computers and creativity. AISB Quarterly, (136), 5–11.
Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In L. Aiello & D. McFarland (Eds.), Social network data analytics (pp. 201–237). Springer.
Moretti, F. (2013). Distant reading. Verso.
Nguyen, D., McGillivray, B., & Yasseri, T. (2020). Emo, love, and god: Making sense of urban dictionary, a crowdsourced online dictionary. Royal Society Open Science, 7(1), 191836. https://doi.org/10.1098/rsos.191836
Nussbaum, M. C. (1997). Cultivating humanity: A classical defense of reform in liberal education. Harvard University Press.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
Phelan, J. (2005). Living to tell about it: A rhetoric and ethics of character narration. Cornell University Press.
Reagan, A. J., Mitchell, L., Kiley, D., Danforth, C. M., & Dodds, P. S. (2016). The emotional arcs of stories are dominated by six basic shapes. EPJ Data Science, 5(1), 31.
Reyes, A., Rosso, P., & Veale, T. (2013). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1), 239–268.
Ryan, M.-L. (2020). Narrative as virtual reality 2: Revisiting immersion and interactivity in literature and electronic media (2nd ed.). Johns Hopkins University Press.
Said, E. W. (1993). Culture and imperialism. Knopf.
Shutova, E. (2011). Design and evaluation of metaphor processing systems. Computational Linguistics, 39(1), 1–5.
Simonton, D. K. (2012). Taking the U.S. Patent Office criteria seriously: A quantitative three-criterion creativity definition and its implications. Creativity Research Journal, 24(2–3), 97–106.
Turner, M. (2014). The origin of ideas: Blending, creativity, and the human spark. Oxford University Press.
Veale, T., & Hao, Y. (2007). Making lexical ontologies functional and context-sensitive. In Proceedings of ACL 2007 Workshop on Cognitive Aspects of Computational Language Acquisition (pp. 57–64).
Wallace, M. (2007). Satire and the novel in eighteenth-century England. Cambridge University Press.
Whissell, C. (2013). Three decades of emotion in the short stories of Ernest Hemingway. Perceptual and Motor Skills, 116(1), 97–106.
Yacobi, T. (1981). Fictional reliability as a communicative problem. Poetics Today, 2(2), 113–126.
