Author:
Kalabikhina Irina,Loukachevitch Natalia,Banin Eugeny,Kolotusha Anton
Abstract
We propose to consider our experience in data use of Russian-language texts of social networks, electronic media, and search engines in demographic analysis. Experiments on the automatic classification of opinions have been carried out. Conversational RuBERT has been used in most cases. The following main scientific results on text data will be described: (1) short-term forecasts of fertility dynamics according to Google trend data, (2) automatic measurement of the demographic temperature of various demographic groups (pronatalists and antinatalists) in social networks, (3) sentiment analysis of reproductive behavior, sentiment analysis of vital behavior in pandemic, sentiment analysis of attitudes toward demographic and epidemiological policy according to social network data, (4) analysis of the arguments of social network users, and (5) analysis of media publications on demographic policy. A description of the created open databases of all these studies will be provided. All of the studies described will contain reflections on the advantages and difficulties of using texts as data in demographic analysis.
Reference81 articles.
1. Gentzkow M, Kelly B, Taddy M. Text as data. Journal of Economic Literature. 2019;(3):535-574
2. Kalpak KK, Arti DK, Dinesh S, Piyush S. A typology of viral ad sharers using sentiment analysis. Journal of Retailing and Consumer Services. 2020;:101739
3. Dinesh KS, Fernandes S. Impact of e-WOM on consumer purchase behaviour through twitter sentiment analysis using Vader and machine learning. AIP Conference Proceedings. 2023;(1):30012
4. Karn AL, Karna RK, Kondamudi BR, et al. Customer centric hybrid recommendation system for E-commerce applications by integrating hybrid sentiment analysis. Electronic Commerce Research. 2023;:279-314
5. Reis BY, Brownstein JS. Measuring the impact of health policies using internet search patterns: The case of abortion. BMC Public Health. 2010;:1-5