Using Topic Generation Model to explore the French Parliamentary Debates during the early Third Republic (1881-1899)

Nicolas Bourgeois (1, 2) , Aurélien Pellet (1, 2) , Marie Puren (1, 2, 3)

1 EPITECH
2 MNSHS - Méthodes Numériques pour les Sciences de l'Humain et de la Société
3 CJM - Centre Jean Mabillon

In this long paper, we use NLP techniques to explore two decades (1881-1899) of parliamentary debates of the French Third Republic (1870-1940), and more specifically to analyse the importance of the army in the political debate. We use Latent Dirichlet Allocation to partition the vocabulary into topics, and then study the distribution of the topic "army" over time. We also examine its connection with other topics, in relation to the main political and military events of the period.

Mots clés: Natural Language Processing; Topic Modelling; Parliamentary Debates; France; Early Third Republic (1881-1899)

Domaines: Histoire; Traitement du texte et du document; Informatique et langage [cs.CL]; Recherche d'information [cs.IR]; Ordinateur et société [cs.CY]

Nicolas Bourgeois, Aurélien Pellet, Marie Puren. Using Topic Generation Model to explore the French Parliamentary Debates during the early Third Republic (1881-1899). Matti La Mela; Fredrik Norén; Eero Hyvönen. DiPaDA 2022 Digital Parliamentary Data in Action 2022. Proceedings of the Digital Parliamentary Data in Action (DiPaDA 2022) Workshop co-located with 6th Digital Humanities in the Nordic and Baltic Countries Conference (DHNB 2022), 3133, , pp.35-51, 2022, CEUR Workshop Proceedings. ⟨hal-03526254v2⟩