Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks

Alena Zaitseva, Ivan Mamaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

The paper discusses the development of a corpus of Russian ministerial posts based on VKontakte social network. The study is aimed at revealing topical structure of ministerial communities. We performed a series of experiments which include LDA topic modeling and automatic topic labeling that help to improve the interpretability of topics. To implement the procedures, we used Python libraries for NLP. Experiments allowed us to find out pivotal topics that the government of Russia covers on social networks nowadays.
Original languageEnglish
Title of host publicationProceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020)
EditorsAlexander Elizarov, Natalia Loukachevitch
Pages32-42
StatePublished - 18 Dec 2020
EventComputational Models in Language and Speech & 16th International Conference on Computational and Cognitive Linguistics - Kazan, Russian Federation
Duration: 12 Nov 202013 Nov 2020

Publication series

NameCEUR workshop proceedings
Volume2780
ISSN (Print)1613-0073

Conference

ConferenceComputational Models in Language and Speech & 16th International Conference on Computational and Cognitive Linguistics
Abbreviated titleCMLS 2020 - TEL 2020
CountryRussian Federation
CityKazan
Period12/11/2013/11/20

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