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Monitoring of Sustainable Development Trends: Text Mining in Regional Media

Author

Listed:
  • Galina Chernyshova

    (Faculty of Computer Science and Information Technologies, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia)

  • Evgeniy Taran

    (Faculty of Computer Science and Information Technologies, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia)

  • Anna Firsova

    (Faculty of Economics, Peoples Friendship University of Russia (RUDN University), 6, Miklukho-Maklaya Str., 117198 Moscow, Russia
    Faculty of Economics, Saratov State University, 83, Astrakhanskaya Str., 410600 Saratov, Russia)

  • Alla Vavilina

    (Faculty of Economics, Peoples Friendship University of Russia (RUDN University), 6, Miklukho-Maklaya Str., 117198 Moscow, Russia)

Abstract

The monitoring of regional development sustainability is closely linked to the development of an indicator system that best meets stakeholders’ requirements, providing a solid foundation for strategic decision-making. In pursuit of progress in achieving the Sustainable Development Goals (SDG), efforts are continuously being undertaken to refine and enhance the indicator framework. Implementing interdisciplinary approaches for a comprehensive assessment of sustainable development in regions allows for a swift expansion and augmentation of data on regional transformations. An important aspect of the study of sustainability at the regional level is the additional possibility of using unstructured news content through text mining methods. The issue of applying natural language processing techniques for Russian-language sources is significant, as a large number of relevant tools are developed for English. Additionally, the analysis of news content has several features that complicate the classification of sentiments of messages with mostly neutral wording. The proposed methodology for processing specific news content in assessing the sustainability of regional development was implemented. An application for data scraping was developed, data were collected taking into account the selected regions and periods, stop word dictionaries were configured, frequency analysis was implemented, and the sentiment analysis of the obtained slices was carried out. For the formed set of news documents related to sustainable development by keywords according to SDGs 1–17, for the regions of the Volga Federal District, a corpus of documents was obtained representing data for 2021, 2022, and 2023 for 14 regions. The analysis of key topics for different areas and periods was carried out using the cosine similarity measure. The developed approach to news analysis allows for increasing the efficiency of monitoring on various topics. This methodology has been tested for systemic and operational assessment in the dynamics of the sustainable development of regions. Text analysis methods within the framework of decision support at the regional level provide the opportunity to identify emerging trends.

Suggested Citation

  • Galina Chernyshova & Evgeniy Taran & Anna Firsova & Alla Vavilina, 2025. "Monitoring of Sustainable Development Trends: Text Mining in Regional Media," Sustainability, MDPI, vol. 17(7), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3122-:d:1625928
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