Intelligent system for clustering users of social networks based on the message sentiment analysis

2023;
: pp. 121 - 138
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University

The main objective of this article is the analysis of the intelligent system for clustering users of social networks based on the messages sentiment analysis. The main goal of this intelligent system is to form a general image of the user of the system by analyzing the sentiment of the data of the user's social networks and their subsequent clustering. An intelligent system was designed, which, using the Identity and Access/Refresh JWT token algorithms, provides fast  and maximally secure registration, authentication and processing of various system user sessions. The main approaches to the sentiment analysis of user messages and other data of various types are described, the principles of LSTM implementation of a recurrent neural network are described, which is very convenient for data analysis, because it works well and remembers the context of messages in the necessary time intervals, which increases the meaningfulness factor of the data analyzed according to the user of the intelligent system. General modern approaches to clustering and the most suitable clustering algorithm k-means is also described, since we will work with an undetermined amount of data each time, which can change significantly according to each individual user, the number of clusters and data processing will change because of this. Due to this, as a result of the work, the creation of a general image of the system user was described thanks to its comprehensive analysis, which made it possible to analyze users and display the corresponding results.

  1. Zhang M., Xu H., Ma N., Pan X. (2022). Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index. Sustainability, No. 14 (16), 344–361. DOI: https://doi.org/10.3390/su141610344.
  2. Vysotska V. (2021). Information Technology for Internet Resources Promotion in Search Systems Based on Content Analysis of Web-Page Keywords. Radio Electronics, Computer Science, Control, No. 3, 133–151.
  3. Antonowicz P., Podpora M., Rut J. (2022). Digital Stereotypes in HMI – The Influence of Feature Quantity Distribution in Deep Learning Models Training. Sensors, No. 22 (18), 673–689. DOI: https://doi.org/ 10.3390/s22186739.
  4. De-Gregorio F., Sung Y. (2010). Understanding attitudes toward and behaviors in response to product placement. Journal of Advertising, No. 39 (1), 83–96. DOI: http://doi.org/10.2753/JOA0091-3367390106.
  5. Kamath A. N., Shenoy S., Subrahmanya K. N. (2022). An overview of investor sentiment: Identifying themes, trends, and future direction through bibliometric analysis. Investment Management & Financial Innovations, No. 19 (3), 229–242. DOI: https://doi.org/10.21511/imfi.19(3).2022.19.
  6. Erkan I. (2016).The influence of e-WOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, No. 4, 47–55.
  7. Asgari T., Daneshvar A., Chobar A. P., Ebrahimi M., Abrahamyan S. (2022). Identifying key success factors for startups With sentiment analysis using text data mining. International journal of Engineering Business Management, No. 14, 435–453. DOI: https://doi.org/10.1177/18479790221131612.
  8. Gao L. (2014). Online consumer behavior and its relationship to website atmospheric induced flow: Insights into online travel agencies in China. Journal of Retailing and Consumer Services, No. 21 (4), 653–655.
  9. Abulhaija S., Hattab S., Abdeen A., Etaiwi W. (2022). Mobile Applications Rating Performance: A Survey. International journal of Interactive Mobile Technologies, No. 16 (19), 133–146. DOI: https://doi.org/10.3991/ ijim.v16i19.32051.
  10. Guidry J. D., Messner M., Jin Y. (2015). From McDonalds fail to Dominos sucks: An analysis of Instagram images about the 10 largest fast food companies. Corporate Communications: An International Journal, No. 20 (3), 344–359.
  11. Bagate R. A., Suguna R. (2022). Sarcasm Detection with and without #Sarcasm: Data Science Approach. International journal of Information Science and Management, No. 20 (4), 1–15.
  12. Salganik M. (2019). Social Research in the Digital Age. Journal of Interactive Marketing, No. 2 (9), 345–358.
  13. Li Q., Li X., Du Y., Fan Y., Chen X. (2022). A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis. Applied Sciences, No. 12 (20), 712–725. DOI: https://doi.org/10.3390/app122010236.
  14. Jeff M., Jennifer R., Catherine J., Elke P. (2014). Managing brand presence through social media: The case of UK football clubs. Internet Research, No. 24 (2), 181–204.
  15. Opiła J. (2022). On Employing of Extended Characteristic Surface Model for Forecasting of Demand in Tourism. Interdisciplinary description of Complex Systems, No. 20 (5), 621–639. DOI: https://doi.org/10.7906/ indecs.20.5.8.
  16. Kudeshia C., Sikdar P., Mittal A. (2016). Spreading love through fan page liking: A perspective on small scale entrepreneurs. Computers in Human Behavior, No. 8 (19), 257–270. DOI: http://doi.org/10.1016/ j.chb.2015.08.003.
  17. Albahli S., Irtaza A., Nazir T., Mehmood A., Ali A., Waleed Albattah W. (2022). A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data. Electronics, No. 11 (20), 341–363. DOI: https://doi.org/10.3390/electronics11203414.
  18. Mousavijad M. (2017). The effect of socialization factors on decision making of teenagers consumers in schools. Journal of School Administration, No. 5 (1), 217–234.
  19. Kim D., Kim Y., Jeong Y.-S. (2022). Graph Convolutional Networks with POS Gate for Aspect-Based Sentiment Analysis. Applied Sciences, No. 12 (19), 101–134. DOI: https://doi.org/10.3390/app121910134.
  20. Parry M. E., Kawakami T., Kishiya K. (2012). The effect of personal and virtual word-of-mouth on tech- nology acceptance. Journal of Product Innovation Management, No. 29 (6), 952–966. DOI: http://doi.org/10.1111/ j.1540-5885.2012.00972.x.
  21. Karyukin V., Mutanov G., Mamykova Z., Nassimova G., Torekul S., Sundetova Z., Negri M. (2022). On the development of an information system for monitoring user opinion and its role for the public. Journal of Big Data, No. 9 (1), 119–145. DOI: https://doi.org/10.1186/s40537-022-00660-w.
  22. Murphy S. T. (2011). Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology, No. 8 (3), 723–739. DOI: http://doi.org/10.1037/0022-3514.64.5.723.
  23. Wang Y., Guo J., Yuan C., Li B. (2022) Sentiment Analysis of Twitter Data. Applied Sciences, No. 12 (8),  157–189. DOI: https://doi.org/10.3390/app122211775
  24. Schmäh M., Wilke T., Rossmann A. (2017). Electronic word of mouth: A systematic literature analysis. Digital Enterprise Computing, 147–158.
  25. Wang Y., Chen Z., Fu C. (2022). Synergy Masks of Domain Attribute Model DaBERT: Emotional Track- ing on Time-Varying Virtual Space Communication. Sensors, No. 22 (21), 450–471. DOI: https://doi.org/ 10.3390/s22218450.
  26. Park J., Ciampaglia G. L., Ferrara F. (2016). Style in the age of Instagram: Predicting success within the fashion industry using social media. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, No. 22 (8), 64–72. DOI: http://doi.org/10.1145/2818048.2820065.
  27. Abbas A. F., Jusoh A., Mas’od A., Alsharif A. H., Ali J. (2022). Bibliometrix analysis of information sharing in social media. Cogent Business & Management, No. 9 (1), 521–543. DOI: https://doi.org/ 10.1080/23311975.2021.2016556.