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Topic Modeling Genre: An Exploration of French Classical and Enlightenment Drama

Authors: Christof Schöch;

Topic Modeling Genre: An Exploration of French Classical and Enlightenment Drama

Abstract

The concept of literary genre is a highly complex one: not only are different genres frequently defined on several, but not necessarily the same levels of description, but consideration of genres as cognitive, social, or scholarly constructs with a rich history further complicate the matter. This contribution focuses on thematic aspects of genre with a quantitative approach, namely Topic Modeling. Topic Modeling has proven to be useful to discover thematic patterns and trends in large collections of texts, with a view to class or browse them on the basis of their dominant themes. It has rarely if ever, however, been applied to collections of dramatic texts. In this contribution, Topic Modeling is used to analyze a collection of French Drama of the Classical Age and the Enlightenment. The general aim of this contribution is to discover what semantic types of topics are found in this collection, whether different dramatic subgenres have distinctive dominant topics and plot-related topic patterns, and inversely, to what extent clustering methods based on topic scores per play produce groupings of texts which agree with more conventional genre distinctions. This contribution shows that interesting topic patterns can be detected which provide new insights into the thematic, subgenre-related structure of French drama as well as into the history of French drama of the Classical Age and the Enlightenment.

Comment: 11 figures

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, 1630-1780, French Drama, topic modeling, J.5, Digital Humanities, Classical Theater, French Literature, Computation and Language (cs.CL), Drama

41 references, page 1 of 5

Blei 2012 Blei, David M. 2012. “Probabilistic Topic Models”. In: Communication of the ACM, 55.4, 77-84.

Blei and McAuliffe 2008 Blei, David M., Jon D. McAuliffe. 2008. “Supervised Topic Models”. In: Neural Information Processing Systems20: 121-128. http://papers.nips.cc/paper/3328-supervised-topic-models.pdf .

Blei et al. 2003 Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2003. “Latent Dirichlet Allocation”. In: Journal of Machine Learning Research3, 993-1022.

Blei et al. 2004 Blei, David M., Tom Griffiths, Michael I. Jordan, and Joshua B. Tenenbaum. 2004. “Hierarchical Topic Models and the Nested Chinese Restaurant Process”. In: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, ed. Sebastian Thrun, Lawrence K. Saul, and Bernhard Schölkopf. Boston, MA: MIT Press.

Blevins 2010 Blevins, Cameron. 2010. “Topic Modeling Martha Ballard's Diary”. In: Historying, http://historying.org /2010/04/01/topic-modeling-martha-ballards-diary/ .

Buntine and Mishra 2014 Buntine, Wray L. and Swapnil Mishra, 2014. “Experiments with Non-parametric Topic Models”. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD '14), New York: ACM, pp. 881-890. http://doi.acm.org/10.1145/2623330.2623691 . [OpenAIRE]

Burnard 2014 Burnard, Lou. 2014. What Is the Text Encoding Initiative? How to Add Intelligent Markup to Digital Resources. Encyclopédie Numérique. Marseille: OpenEdition Press. http://books.openedition.org/oep/426 . [OpenAIRE]

Chang et al. 2009 Chang, Jonathan, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, and David M. Blei. 2009. “Reading Tea Leaves: How Humans Interpret Topic Models”. In: NIPS'09, 288-96.

Du et al. 2006 Du, Lan, W.L. Buntine, and Huidong Jin. 2010. “Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document”. In: IEEE 10th International Conference on Data Mining (ICDM), 148-57, doi:10.1109/ICDM.2010.51 .

Eder et al. 2016 Eder, Maciej, Mike Kestemont, and Jan Rybicki. 2016. “Stylometry with R: A Package for Computational Text Analysis”. In: The R Journal, 16.1, 1-15. https://journal.r-project.org/archive/accepted/ederrybicki-kestemont.pdf .

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    12
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Top 10%
Average
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