Analysis of Users’ Sentiments in Social Media (on the Example of the Astrakhan Region)
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Social Sentiments
Text Mining
Astrakhan Region
Big Data
Content Analysis
Data Visualization

How to Cite

Chernichkin, D., & Krivenko, A. (2023). Analysis of Users’ Sentiments in Social Media (on the Example of the Astrakhan Region). Galactica Media: Journal of Media Studies, 5(3), 145-169.


The article is devoted to the studying of the opinions and sentiments of users of regional communities in the social network VKontakte using methods of machine analysis of text data, supplemented by sociological research methods. In the course of the study, we identified a list of current topics discussed by the inhabitants of the region, determined the most frequently mentioned persons, and analyzed the tone of their mention. Additionally, on the basis of the obtained results, the index of subjective (non-) well-being (ISW) was calculated for each district of the region and a map of the emotional coloring of posts from the communities of the analyzed social network was built. The results of the study can be used to monitor the situation in the region, finding problem areas, elicitation opinion leaders (popular personalities of the region that have a special influence on the opinion of the population), as well as identify the most interesting topics and urgent problems for the population. In perspective, this method of monitoring the social sentiments of the population of the region can be improved by automating the addition of new data to the analytical project. In the future, the addition of mathematical models to the system will make it possible to create graphs for predicting further changes in the region.
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Abdullaeva, R.A. (2015). Analysis of the impact of social networks on the life of modern society. International Journal of Applied and Basic Research, 9(3), 542-546. (In Russian).

Ahmed, A., Scheepers, H., & Stockdale, R. (2014). Social Media Research: A Review of Academic Research and Future Research Directions. Pacific Asia Journal of the Association for Information Systems, 6, 1, 3.

Algan, Y., Murtin, F., Beasley, E., Higa, K., & Senik, C. (2019). Well-being through the lens of the Internet. PLoS ONE, 14(1), e0209562.

API Reference. (In Russian).

Appel, H., Gerlach, A. L., & Crusius, J. (2016). The interplay between Facebook use, social comparison, envy, and depression. Current Opinion in Psychology, 9, 44-49.

Beck, R., Pahlke, I., & Seebach, C. (2014). Knowledge exchange and symbolic action in social media-enabled electronic networks of practice: A multilevel perspective on knowledge seekers and contributors. MIS Quarterly, 38, 4, 1245-1270.

Belenkova, L.M., & Belokonev, S.Yu. (2020). Social networks in information policy: formation of the image of federal executive authorities. Citizen. Elections. Power, 1(15), 92-102. (In Russian).

Bellet, C., & Frijters, P. (2019). Big Data and Well-being. In Helliwell, J., Layard, R., Sachs, J. (Eds.), World Happiness Report 2019. New York: Sustainable Development Solutions Network, 97-122.

Bogdanova, A.V., Aleksandrova, Yu.K., Orlova, V.V., Petrov, E.Yu., & Glazova, V.F. (2022). Study of the dynamics of students' opinions in the context of the transition to online learning based on social network data. Higher education in Russia, 31, 6, 77–91. (In Russian).

Chou, H.-T. G., & Edge, N. (2012).’They Are Happier and Having Better Lives than I Am’: The Impact of Using Facebook on Perceptions of Others’ Lives. Cyberpsychology, behavior, and social networking, 15(2), 117-121.

Ciobanu A., & Androniceanu A. (2018). Integrated human resources activities – the solution for performance improvement in Romanian public sector institutions, Management Research and Practice, 10(3), 60-79.

Dhall, A., Goecke, R., & Gedeon, T. (2015). Automatic group happiness intensity analysis. IEEE Transactions on Affective Computing, 6(1), 13-26. 2015.2397456

Dickinger, A., Arami, M., & Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 1, 4–11.

Durahim, A.O., & Coşkun, M. (2015). # iamhappybecause: Gross National Happiness through Twitter analysis and big data. Technological Forecasting and Social Change, 99, 92–105. 0040-1625

Esiev, E.T. (2021). Internet technologies of political mobilization in Belarusian protests at the pre-election stage. Bulletin of the Moscow State Regional University, 2, 23-37. (In Russian).

Galoyan, O. T., & Erokhina, E.V. (2020). Methods and techniques for big data analysis. Trends in the development of science and education, 62-10, 24-27. (In Russian).

Guba, E. (2018). Big data in sociology: new data, new sociology? Sociological Review, 17, 1, 213-236. (In Russian).

Hao, B., Li, L., Gao, R., Li, A., & Zhu, T. (2014). Sensing Subjective Well-Being from Social Media. In Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, Y.S. (Eds.), Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham, 324-335.

Haseeb, M., Hussai, H.I., Kot, S., Androniceanu, A., & Jermsittiparsert, K. (2019). Role of social and technological challenges in achieving a sustainable competitive advantage and sustainable business performance. Sustainability, 11(14), 3811.

Hills, T., Proto, E., & Sgroi, D. (2019). Historical analysis of national subjective well-being using millions of digitized books. Nature: Human Behaviour, 3 (12), 1271-1275.

Lee, K.-T., Noh, M.-J., & Koo, D.-M. (2013). Lonely people are no longer lonely on social networking sites: The mediating role of self-disclosure and social support. Cyberpsychology, Behavior, and Social Networking, 16 (6), 413-418.

Leonardi, P.M. (2015). Ambient Awareness and Knowledge Acquisition: Using Social Media to Learn “Who Knows What” and “Who Knows Whom”. MIS Quarterly, 39, 4, 747–762.

Liebrecht, C., Hustinx, L., & Van Mulken, M. (2019). The relative power of negativity: the influence of language intensity on perceived strength. Journal of Language and Social Psychology, 38, 2, 170-193.

Maltseva, D.V. (2014). Relational sociology: a new stage in the development of social network analysis or an independent direction? Monitoring, 4(122), 3-14. (In Russian).

Mar'enkov, A.N., & Krivenko, A.I. (2022). Collection and processing of textual data in the context of social sentiment assessment: methodological aspects. Engineering Bulletin of the Don, 7(91), 101-120.

Mische, A. (2011). Relational Sociology. Culture, and Agency. In J. G. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis. SAGE Publications, 80-98.

PDL Help. html (In Russian).

Prokofiev, A.V., & Romanova, M.D. (2017). On some issues of using big data in urban sociology. Actual problems of human development in modern society. Perm: Perm State University. (In Russian).

Ryabchenko, N.A., Gnedash, A.A., Malysheva, O.P., Shestakova, A.A., & Nikolaeva, M.V. (2019). Socio-political content and regional discourse in modern Russia: what citizens discuss online and what candidates for governor propose in their election programs (intersection points and fault lines). South Russian Journal of Social Sciences, 20, 4, 27-48. (In Russian).

Sabatini, F., & Sarracino, F. (2017). Online Networks and Subjective Well‐Being. Kyklos, 70(3), 456-480.

Schwartz, H.A., Sap, M., Kern, M.L., Eichstaedt, J.C., Kapelner, A., Agrawal, M., Blanco, E., Dziurzynski, L., Park, G., Stillwell, D., Kosinski, M., Seligman, M.E., & Ungar, L.H. (2016). Predicting individual well-being through the language of social media. Pacific Symposium on Biocomputing, 21, 516‑527. 49411_0047

Shchekotin, E.V., Kovarzh, G.Yu., Goiko, V.L., Petrov, E.Yu., & Bakulin, V.V. (2020). Assessment of the quality of life of the population of the regions of the Russian Federation on the basis of digital data: methodological aspects. Vectors of well-being: economics and society, 3(38), 138‑156. (In Russian).

Smetanin, S. (2020). The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives. IEEE Access, 8, 110693-110719.

Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., Victor, A., Omori, K., & Allen, M. (2014). Does Facebook make you lonely?: A meta analysis. Computers in Human Behavior, 36, 446-452.

SRL Help. html (In Russian).

Trussler, M., & Soroka, S. (2014). Consumer demand for cynical and negative news frames. The International Journal of Press. Politics, 19, 3, 360-379.

Valenzuela, S., Halpern, D., & Katz, J.E. (2014). Social network sites, marriage well-being and divorce: Survey and state-level evidence from the United States. Computers in Human Behavior, 36, 94-101.

Van Dijk, J. (2006). The network society. social aspects of new media. London, SAGE Publ., 300 p.

Verduyn, P., Ybarra, O., Resibois, M., Jonides, J., & Kross, E. (2017). Do social network sites enhance or undermine subjective well‐being? A critical review. Social Issues and Policy Review, 11(1), 274‑302.

Wu, K., Ma, J., Chen, Z., & Ren, P. (2015). Analysis of Subjective City Happiness Index Based on Large Scale Microblog Data. In Cheng, R., Cui, B., Zhang, Z., Cai, R., & Xu, J. (Eds.), Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science, 9313. Springer, Cham, 365‑377.

Yarmak, O.V., Zharkova, T.V., & Sarkisov, D.G. (2022). The use of big data in interdisciplinary research on the example of the Greater Mediterranean macroregion. Digital Sociology, 5, 3, 24–30. (In Russian).

Zimova, N.S., Fomin, E.V., & Smagina A.A. (2020). Social networks as a new channel for interaction between society and government. scientific result. Sociology and Management, 2, 159-171. (In Russian).

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