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Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks. / Zaitseva, Alena ; Mamaev, Ivan .

Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020). ed. / Alexander Elizarov; Natalia Loukachevitch . 2020. p. 32-42 (CEUR workshop proceedings; Vol. 2780).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Zaitseva, A & Mamaev, I 2020, Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks. in A Elizarov & N Loukachevitch (eds), Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020). CEUR workshop proceedings, vol. 2780, pp. 32-42, Computational Models in Language and Speech & 16th International Conference on Computational and Cognitive Linguistics, Kazan, Russian Federation, 12/11/20. <http://ceur-ws.org/Vol-2780/paper3.pdf>

APA

Zaitseva, A., & Mamaev, I. (2020). Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks. In A. Elizarov, & N. Loukachevitch (Eds.), Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020) (pp. 32-42). (CEUR workshop proceedings; Vol. 2780). http://ceur-ws.org/Vol-2780/paper3.pdf

Vancouver

Zaitseva A, Mamaev I. Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks. In Elizarov A, Loukachevitch N, editors, Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020). 2020. p. 32-42. (CEUR workshop proceedings).

Author

Zaitseva, Alena ; Mamaev, Ivan . / Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks. Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020). editor / Alexander Elizarov ; Natalia Loukachevitch . 2020. pp. 32-42 (CEUR workshop proceedings).

BibTeX

@inproceedings{13d6f9231cba4b9fb316c943c4fb25ae,
title = "Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks",
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.",
author = "Alena Zaitseva and Ivan Mamaev",
year = "2020",
month = dec,
day = "18",
language = "English",
series = "CEUR workshop proceedings",
pages = "32--42",
editor = "Elizarov, {Alexander } and {Loukachevitch }, {Natalia }",
booktitle = "Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020)",
note = "Computational Models in Language and Speech &amp; 16th International Conference on Computational and Cognitive Linguistics, CMLS 2020 - TEL 2020 ; Conference date: 12-11-2020 Through 13-11-2020",

}

RIS

TY - GEN

T1 - Automatic Detection of the Topical Structure of the Ministerial Posts on Social Networks

AU - Zaitseva, Alena

AU - Mamaev, Ivan

PY - 2020/12/18

Y1 - 2020/12/18

N2 - 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.

AB - 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.

UR - http://ceur-ws.org/Vol-2780/

M3 - Conference contribution

T3 - CEUR workshop proceedings

SP - 32

EP - 42

BT - Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020)

A2 - Elizarov, Alexander

A2 - Loukachevitch , Natalia

T2 - Computational Models in Language and Speech &amp; 16th International Conference on Computational and Cognitive Linguistics

Y2 - 12 November 2020 through 13 November 2020

ER -

ID: 71955294