Lakshmi Sonkusale*, Vivek Kumar and Himanshushekhar Chaurasia

Department of Computer Application, ICAR-IASRI, New Delhi, India

*Corresponding Author: Lakshmi Sonkusale, Department of Computer Application, ICAR-IASRI, New Delhi, India.

Received: January 16, 2025; Published: January 28, 2025

Abstract

Climate change remains one of the most urgent challenges facing the global community. As scientific research in this domain grows exponentially, analyzing large volumes of research papers, reports, and articles becomes crucial for identifying emerging trends, key discourse, and underlying patterns in the climate change field. This paper explores the application of text-mining techniques to systematically analyze and categorize the discourse surrounding climate change, leveraging computational approaches to identify trends, topics, and evolving themes from papers. Through web scraping and the extraction of 1000 paper titles from Google Scholar, spanning from 1980 to 2024, we employ a series of text mining methodologies including tokenization, lemmatization, and topic modeling to derive insights into the global research focus on climate change. The findings highlight key themes, regional disparities, and the shifts in the scientific community’s priorities over time, offering valuable implications for policymakers, researchers etc. working towards climate action.

Keywords: Text Mining; Agriculture; Climate change; LDA

References

1. Aggarwal Charu C and ChengXiang Zhai. "A survey of text classification algorithms”. Mining text Data (2012): 163-222.
2. Aversa Dario., et al. "Scoping review (SR) via text data mining on water scarcity and climate change”. Sustainability1 (2022): 70.
3. Balaji K. "Text Mining in Climate Change Communication and Corporate Sustainability Reporting”. Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability. IGI Global (2025): 385-418.
4. Blei David M., et al. "Latent dirichlet allocation”. Journal of Machine Learning Research (2003): 993-1022.
5. Boussalis Constantine and Travis G Coan. "Text-mining the signals of climate change doubt”. Global Environmental Change 36 (2016): 89-100.
6. Dayeen Fazle Rabbi., et al. "A text mining analysis of the climate change literature in industrial ecology”. Journal of Industrial Ecology2 (2020): 276-284.
7. Kherwa, Pooja and Poonam Bansal. "Topic modeling: a comprehensive review”. EAI Endorsed transactions on scalable information systems 24 (2019).
8. Mi Zhewei and Hongwei Zhan. "Text Mining Attitudes toward Climate Change: Emotion and Sentiment Analysis of the Twitter Corpus”. Weather, Climate, and Society2 (2023): 277-287.
9. Moreno Angel-Ivan and Teresa Caminero. "Application of text mining to the analysis of climate-related disclosures”. International Review of Financial Analysis 83 (2022): 102307.
10. Purver M., et al. "Unsupervised topic modelling for multi-party spoken discourse”. In proceedings of the 21^st International Conference on Computational Linguistics and 44^th Annual Meeting of the Association for Computational Linguistics (2006): 17-24.
11. Ray N., et al. "Leveraging Text Mining for Better Policy Making to Combat Climate Change: A Bibliometric Analysis Correlating Precipitation Changes with Agriculture in India”. Annals of Library and Information Studies 71 (2024): 435-444.
12. Sonkusale L., et al. "Exploring the applicability of topic modeling in SARS-CoV-2 literature and impact on agriculture”. Indian Journal of Extension Education 4 (2022): 48-56.
13. Sonkusale L., et al. "Evaluating Text Preprocessing Methods for Discovering Quality Topics to Improve the Information Retrieval Mechanism". Acta Scientific Computer Sciences9 (2023): 03-08.

Citation

Citation: Lakshmi Sonkusale., et al. “Leveraging Text Mining to Analyze Climate Change Discourse and Trends: A Computational Approach". Acta Scientific Agriculture 9.2 (2025): 110-117.

Copyright

Copyright: © 2025 Lakshmi Sonkusale., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.