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Economic and Geographical Analysis of the Current State of the Pulp and Paper Industry of European Russia: How is Text Mining Helpful?

Author

Listed:
  • I. F. Kuzminov

    (Institute for Statistical Studies and Economics of Knowledge, HSE University)

  • P. A. Lobanova

    (Institute for Statistical Studies and Economics of Knowledge, HSE University)

Abstract

— The article considers the place and role of the pulp and paper industry as a key branch of the domestic timber industry complex in the economic and spatial development of modern Russia. It is shown that the sector requires liberalization and stabilization, primarily through moratoriums on policy changes. The need and some existing possibilities for the analysis of non-traditional data sources to obtain a more complete and relevant data of the spatial development of industries is emphasized, from the point of narrow-branch economic and geographical research. Big data and, in particular, a text-mining system that includes millions of open text documents was chosen as a non-traditional source for economic and geographical research. Based on text mining, a semantic and trend map was built, and a list of the most significant and dynamically developing terms (topics) was determined. The topics-drivers were multiplied: on the basis of the word2vec model, complex search conditions were formed, which provide a complete and objective coverage of the analyzed area. Using these search terms, a matrix of operating pulp and paper enterprises in European Russia from sectoral text data sources for 2011–2020 was constructed. Based on a combination of matrix analysis and analysis of a map of operating pulp and paper enterprises, typological groups (belts) of enterprises in European Russia were identified, with the characteristics of size, geographic location, development potential, competitiveness, and risks that are different for each group. All four belts have a clear latitude–longitudinal (diagonal) trend from southwest to northeast. The role of application of big data analysis and, in particular, text mining in economic and geographical research for reasonable and objective conclusions is emphasized. These conclusions can be used to make timely and balanced administrative decisions in the timber industry and pulp and paper industry.

Suggested Citation

  • I. F. Kuzminov & P. A. Lobanova, 2021. "Economic and Geographical Analysis of the Current State of the Pulp and Paper Industry of European Russia: How is Text Mining Helpful?," Regional Research of Russia, Springer, vol. 11(4), pages 477-489, October.
  • Handle: RePEc:spr:rrorus:v:11:y:2021:i:4:d:10.1134_s2079970521040092
    DOI: 10.1134/S2079970521040092
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    References listed on IDEAS

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    1. He, Wu & Zha, Shenghua & Li, Ling, 2013. "Social media competitive analysis and text mining: A case study in the pizza industry," International Journal of Information Management, Elsevier, vol. 33(3), pages 464-472.
    2. Ian Keay, 2015. "Immunity from the resource curse? The long run impact of commodity price volatility: evidence from Canada, 1900–2005," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 9(3), pages 333-358, september.
    3. Andrei P Kirilenko & Svetlana Stepchenkova, 2018. "Tourism research from its inception to present day: Subject area, geography, and gender distributions," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-20, November.
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