Cover not available
Part of
Crossing Boundaries through Corpora: Innovative corpus approaches within and beyond linguistics
Edited by Sarah Buschfeld, Patricia Ronan, Theresa Neumaier, Andreas Weilinghoff and Lisa Westermayer
[Studies in Corpus Linguistics 119] 2024
► pp. 62–98

Chapter 4
Digital Dickens

An automated content analysis of Charles Dickens’ novels

| University of Zurich
This investigation employs computational linguistic methods such as document classification, topic modelling, and distributional semantics to scrutinize eight novels by Charles Dickens, uncovering dimensions of social criticism, literary realism, and narrative structures. While affirming positive results for automated analysis of social criticism, the study emphasizes that it could discover differing associations only due to semantic abstraction, which distributional semantics, word embeddings, and topic modelling can offer. Literary realism is successfully traced through detailed descriptions and everyday activities. Plotting plots with computational linguistic methods, specifically conceptual maps with textplot, shows promise but requires refinement. The study shows that current methods in content analysis offer new possibilities for literary analysis and digital humanities.
Keywords: computational linguistics, digital humanities, Charles Dickens, document classification, topic modelling, distributional semantics, conceptual maps
Article outline
  • 1.Introduction
  • 2.Motivation and background
    • 2.1Distributional semantics
    • 2.2Content analysis
    • 2.3Dickens’ visions and style
  • 3.Materials
    • 3.1A corpus of Dickens novels
    • 3.2CLMET 3.0 Corpus
    • 3.3Combined corpus
  • 4.Methods
    • 4.1Document classification
    • 4.2Topic modelling
    • 4.3Conceptual maps
    • 4.4Distributional semantics
    • 4.5Comparison and triangulation of methods
  • 5.Results
    • 5.1Poverty in Dickens
      • 5.1.1Frequency
      • 5.1.2Document classification
      • 5.1.3Distributional semantics
      • 5.1.4Topic modelling
    • 5.2Literary realism
      • 5.2.1Distributional semantics
      • 5.2.2Topic modelling
      • 5.2.2Conceptual maps
      • 5.2.3Plotting plots
  • 6.Conclusion
  • Acknowledgements
  • Notes
  • References
This content is being prepared for publication; it may be subject to changes.
References (37)
References
Baroni, Marco, Dinu, Georgiana & Kruszewski, Germán. 2014. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Kristina Toutanova & Hua Wu (eds), 238–247. Stroudsburg PA: ACL. DOI logoGoogle Scholar
Betts, Jennifer. 2020. What is realism in literature? Elements and examples. Yourdictionary. 25 October 2020. <[URL]> (14 April 2023).Google Scholar
Blei, David. 2012. Probabilistic topic models. Communications of the ACM 55(4): 77–84. DOI logoGoogle Scholar
Buzan, Tony & Buzan, Barry. 1993. The Mind Map Book: How to Use the Radiant Thinking to Maximize Your Brain’s Untapped Potential. London: Penguin.Google Scholar
Deerwester, Scott, Dumais, Susan T., Furnas, George W., Landauer, Thomas K. & Harshman, Richard. 1990. Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6): 391–407. DOI logoGoogle Scholar
De Smet, Hendrik. 2005. A corpus of Late Modern English. ICAME Journal 29: 69–82.Google Scholar
Eve, Martin Paul. 2022. The Digital Humanities and Literary Studies. Oxford: OUP. DOI logoGoogle Scholar
Evert, Stefan 2006. How random is a corpus? The library metaphor. Zeitschrift für Anglistik und Amerikanistik 54(2): 177–190. DOI logoGoogle Scholar
Firth, John Rupert. 1957. A synopsis of linguistic theory 1930–1955. Studies in Linguistic Analysis: 1–32.Google Scholar
Fitzmaurice, Suan, Robinson, Justyna A., Alexander, Marc, Hine, Iona C., Mehl, Seth & Dallachy, Fraser. 2017. Linguistic {DNA}: Investigating Conceptual Change in Early Modern English Discourse. Studia Neophilologica: 1–18. DOI logoGoogle Scholar
Gries, Stefan T. 2015. The most under-used statistical method in corpus linguistics: Multi-level (and mixed-effects) models. Corpora 10: 95–125. DOI logoGoogle Scholar
Grimmer, Justin & Stewart, Brandon. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21(3): 267–297. DOI logoGoogle Scholar
Harris, Zellig. 1968. Mathematical Structures of Language. New York NY: Wiley.Google Scholar
. 1970. Distributional structure. In Papers in Structural and Transformational Linguistics [Formal Linguistics Series], 775–794. Dordrecht: Springer. DOI logoGoogle Scholar
Hundt, Marianne, Schneider, Gerold & Seoane, Elena. 2016. The use of the be-passive in academic Englishes: local versus global language in an international language. Corpora 11(1): 31–63. DOI logoGoogle Scholar
Jacomy, Mathieu, Venturini, Tommaso, Heymann, Sebastien & Bastian, Mathieu. 2014. ForceAtlas2. A Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PLOS ONE 9(6): e98679. < DOI logo>Google Scholar
Janda, Laura A. 2013. Cognitive Linguistics: The Quantitative Turn. Berlin: Mouton de Gruyter. DOI logoGoogle Scholar
Jurafsky, Daniel & Martin, James H. 2009. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics, 2nd edn. Upper Saddle River NJ: Prentice-Hall.Google Scholar
Kailash, Sudha. 2012. Charles Dickens as a social critic. International Journal of Research in Economics & Social Sciences 2(8): 1–51.Google Scholar
Karlgren, Jussi & Sahlgren, Magnus. 2001. From words to understanding. In Foundations of Real-World Intelligence, 294–308. Stanford CA: CSLI.Google Scholar
Kaufman, Micki. 2020. “Everything on paper will be used against me.” Quantifying Kissinger. Quantifying Kissinger. <[URL]> (7 January 2023).Google Scholar
Mahlberg, Michaela. 2013. Corpus Stylistics and Dickens’s Fiction [Routledge Advances in corpus linguistics Series 14]. London: Routledge. DOI logoGoogle Scholar
McClure, David. 2015a. Textplot Refresh. <[URL]>Google Scholar
. 2015b. Textplot. <[URL]> (29 May 2024).Google Scholar
Mikolov, Tomas, Chen, Kai, Corrado, Greg & Dean, Jeffrey. 2013. Efficient estimation of word representations in vector space. arXiv 1301–3781. <[URL]> (29 May 2024).Google Scholar
Moretti, Franco. 2013. Distant Reading. London: Verso.Google Scholar
Munafò, Marcus R. & Smith, George Davey. 2018. Robust research needs many lines of evidence. Nature 553: 399–401. DOI logoGoogle Scholar
Oakes, Michael P. 2014. Literary Detective Work on the Computer [Natural Language Processing 12]. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Rayson, Paul. 2008. From key words to key semantic domains. International Journal of Corpus Linguistics 13(4): 519–549. DOI logoGoogle Scholar
Reed, John R. 2010. Dickens’ Hyperrealism. Columbus OH: Ohio State University Press.Google Scholar
Sahlgren, Magnus. 2006. The Word-Space Model: Using distributional Analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. PhD disseration, Stockholm University.Google Scholar
Schwartz, H. Andrew & Ungar, Lyle H. 2015. Data-driven content analysis of social media: A systematic overview of automated methods. The ANNALS of the American Academy of Political and Social Science 659(1): 78–94. DOI logoGoogle Scholar
Shadrova, Anna. 2021. Topic models do not model topics: Epistemological remarks and steps towards best practices. Journal of Data Mining and Digital Humanities 2021. DOI logoGoogle Scholar
Smith, Nathaniel J. & Levy, Roger. 2013. The effect of word predictability on reading time is logarithmic. Cognition 128(3): 302–319. DOI logoGoogle Scholar
Wittgenstein, Ludwig & Anscombe, Gertrude Elizabeth Margret. 1953. Philosophical Investigations. New York NY: Macmillan.Google Scholar
Zottola, Angela. 2020. Corpus linguistics and digital humanities. Intersecting paths. A case study from Twitter. América Crítica 4(2): 131–141. DOI logoGoogle Scholar
Zucchini, Walter. 2003. Applied smoothing techniques — Part 1: Kernel density estimation. <[URL]> (29 May 2024).Google Scholar