Topic Modeling Genre: An Exploration of French Classical and Enlightenment Drama
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 0 of its 44 references that are in the map. First posted on arXiv 24 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 1; 2026: 3.
9 papers.
- 2023Computational thematics: Comparing algorithms for clustering the genres of literary fictioncited by 5
- 2023Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAIcited by 3
- 2022Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literaturecited by 2
- 2021Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modellingcited by 1
- 2025FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fictioncited by 1
- 2023Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Propertiescited by 0
- 2026Hybrid topic modelling for computational close reading: Mapping narrative themes in Pushkin's Evgenij Onegincited by 0
- 2026Economic Transformation and Cultural Change: Evidence from Two Centuries of French Dramacited by 0
- 2026Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Workscited by 0
- 2026Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works10 Jul 2026
- 2026Economic Transformation and Cultural Change: Evidence from Two Centuries of French Drama19 Jun 2026
- 2026Hybrid topic modelling for computational close reading: Mapping narrative themes in Pushkin's Evgenij Onegin20 Mar 2026
- 2025FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction23 Oct 2025
- 2023Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI13 Oct 2023
- 2023Computational thematics: Comparing algorithms for clustering the genres of literary fiction18 May 2023
- 2023Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Properties16 Feb 2023
- 2022Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature12 Jan 2022
- 2021Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling24 Dec 2021