Stylometry and Deep Learning: A case study on Milan Kundera's Le Livre du rire et de l'oubli

This study aims to uncover the prototypical linguistic elements and patterns of Kundera's prose in his novel Le Livre du rire et de l'oubli (1979, Gallimard). The exploration employs statistical and machine learning techniques, including the application of Hyperbase in both its web and standard versions. Hyperbase provides deep learning features for text classification tasks (Savoy 2015; Tuzzi & Cortelazzo 2018), based on convolutional neural networks (Kalchbrenner et al. 2014; Kim 2014) which go beyond the process of convolution and incorporate an innovative deconvolution mechanism that extracts key linguistic markers essential for classification purposes (Vanni et al. 2018; Mayaffre & Vanni 2021). The training of the Hyperbase deep learning model involves an extensive corpus containing novels by 36 authors, including Kundera, thus encompassing the French literature landscape from 1960-2014. The study leads to the identification of linguistic markers related to vocabulary, morphosyntax, lexical and grammatical patterns, and lexico-grammatical structures. These markers are then examined to reveal the underlying aesthetic intentions of the author. The conclusion focuses on the contribution of deep learning and statistics in the context of this qualitative linguistic study of a literary text.