TY - GEN

T1 - Topic Modeling of Russian-Language Texts Using the Parts-of-Speech Composition of Topics (on the Example of Volunteer Movement Semantics in Social Media)

AU - Мальцева, Анна Васильевна

AU - Шилкина, Наталья Егоровна

AU - Евсеев, Евгений Александрович

AU - Махныткина, Олеся

AU - Матвеев, Михаил Сергеевич

N1 - Publisher Copyright: © 2021 FRUCT. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5/12

Y1 - 2021/5/12

N2 - The article presents a new approach to thematic modeling of texts - this is thematic modeling based on part-of-speech topics. We do not consider parts of the speech as a gnoseological concept that reflects the way in which language is formally classified. We believe that parts of speech are within the language competence of the person and are used in the process of communication, performing a certain function in the communication process. The essence of thematic modeling is seen as the creation of semantic models of the text corpus. The goal is to study the speech representation of modern movements and communities. The hypothesis is that the forums of a social movement reflect its characteristics, the nature, and activities of this movement. Three groups of the Russian social media VKontakte were chosen as an empirical object: 'All for the Victory!', 'Center of (City) Volunteers of St. Petersburg,' 'Volunteers of St. Petersburg.' Topic modeling was carried out using the latent Dirichlet allocation (LDA) method, implemented in the Gensim package along with the Mallet implementation. Model quality validation was carried out using the coherence coefficient. The described approach to the analysis of web texts of volunteer semantics based on the part-of-speech composition of topics made it possible to identify signs that characterize group identity, emotionality, and joint activities of Russian volunteers.

AB - The article presents a new approach to thematic modeling of texts - this is thematic modeling based on part-of-speech topics. We do not consider parts of the speech as a gnoseological concept that reflects the way in which language is formally classified. We believe that parts of speech are within the language competence of the person and are used in the process of communication, performing a certain function in the communication process. The essence of thematic modeling is seen as the creation of semantic models of the text corpus. The goal is to study the speech representation of modern movements and communities. The hypothesis is that the forums of a social movement reflect its characteristics, the nature, and activities of this movement. Three groups of the Russian social media VKontakte were chosen as an empirical object: 'All for the Victory!', 'Center of (City) Volunteers of St. Petersburg,' 'Volunteers of St. Petersburg.' Topic modeling was carried out using the latent Dirichlet allocation (LDA) method, implemented in the Gensim package along with the Mallet implementation. Model quality validation was carried out using the coherence coefficient. The described approach to the analysis of web texts of volunteer semantics based on the part-of-speech composition of topics made it possible to identify signs that characterize group identity, emotionality, and joint activities of Russian volunteers.

UR - http://www.scopus.com/inward/record.url?scp=85107417144&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/9767b1bd-376e-35b3-a34d-bfed9a7b4438/

U2 - 10.23919/fruct52173.2021.9435475

DO - 10.23919/fruct52173.2021.9435475

M3 - Conference contribution

AN - SCOPUS:85107417144

T3 - Conference of Open Innovation Association, FRUCT

SP - 247

EP - 253

BT - Proceedings of the 29th Conference of Open Innovations Association FRUCT, FRUCT 2021

A2 - Balandin, Sergey

A2 - Koucheryavy, Yevgeni

A2 - Tyutina, Tatiana

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Tampere, Finland

T2 - 29th Conference of Open Innovations Association FRUCT, FRUCT 2021

Y2 - 12 May 2021 through 14 May 2021

ER -