The Measurement of Demographic Temperature Using the Sentiment Analysis of Data from the Social Network VKontakte

Mathematics

EvgeniyPetrovich Banin¹  IrinaEvgenievna Kalabikhina²  AntonVasilyevich Kolotusha²  ImiliyaAbduselimovna Abduselimova²  GermanAndreevich Klimenko² 

[1] Department of Applied Mechanics, Faculty of Robotics and Complex Automation, Bauman Moscow State Technical University, Baumanskaya 2-ya st., 5/1, 105005 Moscow, Russia;Population Department, Faculty of Economics, Lomonosov Moscow State University, Leninskije Gory, GSP-1, 119991 Moscow, Russia;

关键词: reproductive attitudes;    demographic temperature;    sentiment analysis;    big data;    Latent Dirichlet Allocation;    VKontakte;   

DOI  :  10.3390/math9090987

来源: DOAJ

【 摘 要 】

Social networks have a huge potential for the reflection of public opinion, values, and attitudes. In this study, the presented approach can allow to continuously measure how cold “the demographic temperature” is based on data taken from the Russian social network VKontakte. This is the first attempt to analyze the sentiment of Russian-language comments on social networks to determine the demographic temperature (ratio of positive and negative comments) in certain socio-demographic groups of social network users. The authors use generated data from the comments to posts from 314 pro-natalist groups (with child-born reproductive attitudes) and eight anti-natalist groups (with child-free reproductive attitudes) on the demographic topic, which have 9 million of users from all over Russia. The algorithm of the sentiment analysis for demographic tasks is presented in the article. In particularly, it was found that comments under posts are more suitable for analyzing the sentiment of statements than the texts of posts. Using the available data in two types of groups since 2014, we find an asynchronous structural shift in comments of the corpuses of pro-natalist and anti-natalist thematic groups. Interpretations of the evidences are offered in the discussion part of the article. An additional result of our work is two open Russian-language datasets of comments on social networks.

【 授权许可】

Unknown