[en] A community discourse can be analyzed through texts written by participants of the community since it is expressed in those texts in various ways. For large communities, the sheer amount of texts generated limits the ability of human researcher to comprehend unique features of a discourse. But modern Machine Learning algorithms are able to process large amounts of text thus aiding the human researcher in investigation. In this study we offer:
1. A large corpus made from three types of text: writings of Russian pro-government and opposition activists and neutral texts without political coloring;
2. A modern word-level Recurrent Neural Network-based approach for unsupervised detection of discourse-specific linguistic.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Kirillov, Bogdan; Skolkovo Institute of Science and Technology
Petrovskaya, Aleksandra; Higher School of Economics
SERGEEVA, Anastasia ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Cognitive Science and Assessment
Langue du document :
Anglais
Titre :
Computational Investigation of Linguistic Markers in Discourse of Political Adversaries via Interpretation of Recurrent Neural Network
Date de publication/diffusion :
2018
Nom de la manifestation :
The Fourth St. Petersburg Winter Workshop on Experimental Studies of Speech and Language
