Mapping Climate Themes From 2008-2021-An Analysis of Business News Using Topic Models

• Published in: IEEE access Vol. 11; pp. 26554 - 26565
• Main Authors: Umamaheswaran, Swarnalakshmi, Dar, Vandita, Sharma, Eliza, Kurian, Jikku Susan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Biological system modeling; Business; Climate change; computational social sciences; Datasets; Decision making; Emitters; experiment; Matrix decomposition; media; Meteorology; News media; NLP; Performance evaluation; Resource management; Social sciences; topic models; India
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3256530

India and other developing economies are receiving more attention in the context of climate change due to their rapid rates of economic expansion and large populations. In terms of absolute emissions, India surpassed China and the U.S. in 2018 to become the third-largest emitter. Solving this wicked problem calls for climate action across the stakeholder spectrum involving governments, business communities, and citizens. While extant literature has focused significantly on the role of governments and individual perceptions, the business sector needs to be more represented. In this study, we consider business news media as a platform that reflects the industry engagement in climate change and as a source of information on climate change for business decision-makers. Hence, understanding the topic and themes in the nexus of climate and business is important to evaluate the business sector's stance towards climate change and how it has evolved. This work explores business news related to climate change using natural language techniques. We first experiment with three topic-modeling techniques, such as LDA, NMF, and BERTopic, on the business news and two more benchmark news datasets. Our test data is derived from digital news archives of 'The Economic Times- India's leading business news daily. We evaluate the performance based on quantitative metrics commonly used for topic models. We choose the algorithm that provides the highest precision for climate-specific information represented by the test dataset. We then apply the algorithm with the best performance, as evaluated by the experiment, to a large corpus of Indian climate news from E.T. spanning from 2008 -2021. We present how different themes, including industry engagement, evolved over the last two decades. The results suggest that climate cooperation has the highest contribution in the corpus, with other themes on resource management, energy and business gaining traction in recent years.