Treść głównego artykułu


This publication aims to map the environmental sustainability discourse on Twitter. This will be achieved through two commonly used methods of natural language processing; topic modelling, which is used to uncover hidden themes in the document collection, and sentiment analysis, which is used to detect the attitudes of the authors of the text towards a particular attitude. The exploration of communication can provide an opportunity to find a solution to a multifaceted problem in order to protect our common future.

Szczegóły artykułu

Jak cytować
Tóth, T. E. (2022). Analysis of the Twitter discourse on sustainability using natural language processing . Edukacja Ekonomistów I Menedżerów, 62(4).


  1. Attenborough, D., & Hughes, J. (2020). A Life on Our Planet: My Witness Statement and a Vision for the Future. London: Ebury Publishing.
  2. Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12–03. Rome, FAO. Retrieved from: http:// (accessed: 29.03.2021).
  3. Ballestar, M. T., Cuerdo-Mir, M., & Freire-Rubio, M. T. (2020). The Concept of Sustainability on Social Media: A Social Listening Approach. Sustainability, 12 (5), 2122. MDPI AG.
  4. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
  5. Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll. PLoS ONE, 10 (8), e0136092. DOI:10.1371/journal.pone.0136092
  6. Dahal, B., Kumar, S. A. P., & Li, Z. (2019). Topic modelling and sentiment analysis of global climate change tweets. Soc. Netw. Anal. Min., 9 (24) s13278-019-0568–8
  7. Dasgupta, P., & McKenzie, E. (2020). The Dasgupta Review – Independent Review on the Economics of Biodiversity Interim Report. Retrieved from: https://assets.publishing. The_Economics_of_Biodiversity_The_Dasgupta_Review_Interim_Report.pdf (accessed: 28.01.2021).
  8. Denny, M., & Spirling, A. (2018). Text Preprocessing or Unsupervised Learning: Why It Matters, When It Misleads, And What to Do About It. Political Analysis, 26 (2), 168–189. DOI:10.1017/pan.2017.44
  9. Drews, S., & Miklós, A. (2016). Degrowth: A “Missile Word” That Backfires? Ecological Economics, 126, 182–187. DOI: 10.1016/j.ecolecon.2016.04.001
  10. Drews, S., & Reese, G. (2018). “Degrowth” vs. Other Types of Growth: Labelling Affects Emotions but Not Attitudes”. Environmental Communication, 12 (6), 763–772. DOI: 10.1080/17524032.2018.1472127
  11. Evans, J. A., & Aceves, P. (2016). Machine translation: mining text for social theory.
  12. Annu. Rev. Sociol., 42 (1), 21–50.–074206
  13. FABLE (2020). Pathways to Sustainable Land-Use and Food Systems. 2020 Report of the FABLE Consortium. Laxenburg and Paris: International Institute for Applied Systems Analysis (IIASA) and Sustainable Development Solutions Network (SDSN). https://
  14. Furman, E., Häyhä, T., & Hirvilammi, T. (2018) A future the planet can accommodate.
  15. SKYKE & SITRA, 2. ISBN: 978-952-11-4938-2. Retrieved from: (accessed: 08.03.2021).
  16. Hutto, C. J., & Gilbert, E. (2015). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014.
  17. Intergovernmental Panel on Climate Change – The Fifth and the Sixth Assessment Cycle (2020). Retrieved from: (accessed: 06.03.2021).
  18. Kirilenko, A. P., & Stepchenkova, S. O. (2014). Public microblogging on climate change: One Year off Twitter worldwide. Global Environ. Change. gloenvcha.2014.02.008
  19. Németh, R., Katona, E. R., & Kmetty, Z. (2020). Az automatizált szövegelemzés perspektívája a társadalomtudományokban [Perspectives on automated text analysis in the social sciences]. SZOCIOLÓGIAI SZEMLE, 30 (1), 44–62. ISSN 1216–2051.
  20. Nik-Bakht, M., & El-Diraby, T. E. (2016). Sus-tweet-ability: Exposing public communities perspective on sustainability of urban infrastructure through online social media. International Journal of Human-Computer Studies, 89, 54–72, ISSN 1071–5819. https://
  21. Radi, S. A., & Shokouhyar, S. (2021). Toward consumer perception of cellphones sustainability: A social media analytics. Sustainable Production and Consumption, 25,217–233, ISSN 2352–5509.
  22. Raworth, K. (2017). Doughnut economics: seven ways to think like a 21st-century economist. London: Random House.
  23. Rockström, J., & Klum, M. (2015). Big world, small planet: Abundance within planetary boundaries. USA: Yale University Press.
  24. Stevens, K., Kegelmeyer, P., Andrzejewski, D., & Buttler, D. (2012). Exploring topic coherence over many models and many topics. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL ‘12). Association for Computational Linguistics, USA, 952–961.