Email this article The application of the actor dominance gene algorithm in determining the parameters for information environment analysis
- Authors: Pankova L.V1, Starchenkova O.D1
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Affiliations:
- Peter the Great St. Petersburg Polytechnic University
- Issue: No 5 (2025)
- Pages: 207-219
- Section: Articles
- URL: https://ogarev-online.ru/2500-3747/article/view/369473
- ID: 369473
Cite item
Abstract
the relevance of this study is driven by the need to develop and implement intelligent algorithms for analyzing the information environment of a company's economic activity. In the context of the digital economy, organizations operate within a complex and dynamic information environment that directly affects their efficiency and competitiveness. The actor-based gene domination algorithm proposed in this article is aimed at improving the accuracy and effectiveness of analyzing the key parameters of this environment. The purpose of the research is to develop a tool for analyzing the information environment of a company’s economic activity (hereinafter referred to as the information environment interaction tool) by identifying the essential semantic components of textual information using methods of quantification and constructing a parametric structure of algorithmic constraints within the framework of quality management methodology. The application of this approach enables the identification of dominant information flows, the assessment of the influence of internal and external actors on economic activity, and the formation of well-founded managerial decisions based on big data processing. The results of the study contribute to the advancement of methods for analyzing information interactions in the digital space and to the automation of monitoring and evaluation processes regarding the state of a company’s information environment. The work was carried out within the framework of the project “Development of a methodology for the formation of an instrumental base for the analysis and modeling of spatial socio-economic development of systems in the context of digitalization based on internal reserves” (FSEG-2023-0008).
About the authors
L. V Pankova
Peter the Great St. Petersburg Polytechnic University
Email: pankova_lv@spbstu.ru
O. D Starchenkova
Peter the Great St. Petersburg Polytechnic University
References
- Rainie H., Wellman B. Networked: the new social operating system. Cambridge, MA: MIT Press, 2012. T. 10.
- Horsmann T., Zesch T. LTL-UDE EmpiriST 2015: tokenization and PoS tagging of social media text. Proceedings of the 10th Web as Corpus Workshop. 2016. P. 120 – 126.
- Box G.E.P., Cox D.R. An analysis of transformations // Journal of the Royal Statistical Society Series B: Statistical Methodology. 1964. T. 26. No. 2. P. 211 – 243.
- Singh S., Kumar K., Kumar B. Sentiment analysis of Twitter data using TF-IDF and machine learning techniques. 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) // IEEE, 2022. Vol. 1. P. 252 – 255.
- Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation // Journal of Machine Learning Research. 2003. T. 3. P. 993 – 1022.
- Бондарчук Д.В. Алгоритмы интеллектуального поиска на основе метода категориальных векторов: специальность 05.13.17 «Теоретические основы информатики»: дис. … канд. физ.-матем. наук. Екатеринбург: Уральский государственный университет путей сообщения, 2016. 141 с.
- Richter S., Sundberg C. A formula for the local Dirichlet integral // Michigan Mathematical Journal. 1991. Vol. 38. No. 3. P. 355 – 379.
- Rousseeuw P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis // Journal of Computational and Applied Mathematics. 1987. Vol. 20. P. 53 – 65.
- Rodionov D.G., et al. Information environment quantifiers as investment analysis basis // Economies. 2022. Vol. 10. No. 10. P. 232 – 254.
- Rodionov D.G., et al. The information environment cluster distribution of the regional socio-economic systems in transition economy. International Scientific Conference “Digital Transformation on Manufacturing, Infrastructure & Service”. Cham: Springer Nature Switzerland, 2022. P. 203 – 217.
- Rodionov D.G., et al. Modeling changes in the enterprise information capital in the digital economy // Journal of Open Innovation: Technology, Market, and Complexity. 2021. Vol. 7. No. 3. P. 166 – 189.
- Rodionov D.G., et al. A new model for creating digital avatars of applicants based on social media data // International Journal of eBusiness and eGovernment Studies. 2023. T. 15. No. 1. P. 324 – 341.
- Rodionov D.G., et al. Modeling a digital avatar of car drivers based on the quantification of the information environment // International Conference on Next Generation Wired/Wireless Networking. Cham: Springer Nature Switzerland. 2023. P. 170 – 180.
- Родионов Д.Г., Конников Е.А., Конникова О.А., и др. Влияние социально-экономических факторов на проникновение интернет-маркетинга в различных странах // Мягкие измерения и вычисления. 2024. № 1. С. 48 – 61.
- Родионов Д.Г., Конников Е.А., Пашинина П.А., Шаныгин С.И. Тематическое моделирование информационной среды медиакомпаний: инструментальный комплекс LDA-TF-IDF. Мягкие измерения и вычисления. 2024. № 3. С. 72 – 84.
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