S. A. Slastnikov, L. F. Zhukova, I. V. Semichasnov Application for Data Retrieval, Analysis, and Forecasting in Social Networks
S. A. Slastnikov, L. F. Zhukova, I. V. Semichasnov Application for Data Retrieval, Analysis, and Forecasting in Social Networks

In this article, we present a web service designed for searching, extracting, and analyzing data from social networks and messengers, demonstrating its application for studying communities within the "VKontakte" social network. The web service enables the identification of typical user profiles within communities, the assessment of emotional sentiment in posts and comments, as well as the forecasting of community development trends. The described web service boasts extensive functional capabilities and an original neural network model for classifying texts of varying lengths based on emotional sentiment. Examples of the tool's usage are showcased in the analysis of the development of car brand communities. The analysis encompasses millions of subscriber audiences, tens of thousands of posts, and hundreds of thousands of comments, thereby affirming the relevance of the samples and the credibility of the results.


social network, communities, data analysis, neural network, profile, publication, emotional sentiment, tone, forecasting.

PP. 97-108.

DOI 10.14357/20718632240110 


1. Smetanin S. Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki. Mathematics. 2022;10(16):2497. Available
from: [Accessed 19 October 2022].
2. Kovaleva Yu.V., Zhuravlev A.L. Social Mood and Subjectivity of Networks Community during the Pandemic COVID-19: Using the Example of Social Network Twitter. Federal State Financed Establishment of Science Institute of psychology. 2020;2(18):151-188 (In Russ.). doi:10.38098/ipran.sep.2020.18.2.005
3. Dokuka S.V., Valeeva D.R. Statistical Models for Analysis of Social Network Dynamics in Educational Studies. Voprosy Obrazovaniya. 2015;1:201-2013. (In Russ.) doi: 10.17323/1814-9545-2015-1-201-213
4. Social networks as a tool for studying the psychological portrait of the consumer. Available from:
izucheniya-psihologicheskogo-portreta-potrebitelya. [Accessed 19 October 2022].
5. Personality analysis on social networks as an effective method of recruitment. Available from::
kadrov. [Accessed 19 October 2022].
6. Ilić S. et al. Deep contextualized word representations for detecting sarcasm and irony/ arXiv preprint arXiv:1809.09795.2018.
7. Ke G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems. 2017; 30. doi: 10.5555/3294996.3295074.
8. Sparck Jones K. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation. 1972;28(1):11-21.doi: 10.1108/eb026526.
9. Rogers A. et al. RuSentiment: An enriched sentiment analysis dataset for social media in Russian. Proceedings of the 27th international conference on computational linguistics. – 2018. – С. 755-763.
10. Sidorov N., Slastnikov S. Some Features of Sentiment Analysis for Russian Language Posts and Comments from Social Networks. Journal of Physics: Conference Series. 2021;1740(1): 012036. doi: 10.1088/1742-6596/1740/1/012036.
11. Blinov V., Bolotova-Baranova V., Braslavski P. Large dataset and language model fun-tuning for humor recognition. Proceedings of the 57th annual meeting of the association for computational linguistics. 2019;4027-4032. doi: 10.18653/v1/P19-1394.
12. Pikabu. Available from: [Accessed 29 October 2022].
13. Leaders and outsiders of the Russian car market in 2021. Available from: [Accessed 23 October 2022].
14. The ideal length of publications for social networks. Available from:
[Accessed 24 October 2022].
15. KPI in SMM: how to evaluate the effectiveness of promotion in social networks? Available from:
prodvizheniya-v-socialnyh. [Accessed 24 October 2022].
16. Social media and Media monitoring and Analysis system. Available from: [Accessed 28 October 2022].
17. Medialogy: monitoring of mass media and social networks, a tool for evaluating the effectiveness of communications. Available from: [Accessed 28 October 2022].
18. Kostenetskiy P.S., Chulkevich R.A., Kozyrev V.I. HPC Resources of the Higher School of Economics. Journal of Physics: Conference Series. 2021;1740(1): 012050. doi: 10.1088/1742-6596/1740/1/012050.

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