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ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Haein Lee1, Seon Hong Lee1, Kyeo Re Lee2, Jang Hyun Kim3,*

1 Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, 03063, Korea
2 Center for SW Education, Hanyang University, Ansan, 15588, Korea
3 Department of Interaction Science/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, 03063, Korea

* Corresponding Author: Jang Hyun Kim. Email: email

Computers, Materials & Continua 2023, 75(3), 6023-6037. https://doi.org/10.32604/cmc.2023.039104

Abstract

Environmental, social, and governance (ESG) factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value. Recently, non-financial indicators have been considered as important for the actual valuation of corporations, thus analyzing natural language data related to ESG is essential. Several previous studies limited their focus to specific countries or have not used big data. Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG. To address this problem, in this study, the authors used data from two platforms: LexisNexis, a platform that provides media monitoring, and Web of Science, a platform that provides scientific papers. These big data were analyzed by topic modeling. Topic modeling can derive hidden semantic structures within the text. Through this process, it is possible to collect information on public and academic sentiment. The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic (BERTopic)—a state-of-the-art topic-modeling technique. In addition, changes in subject patterns over time were considered using dynamic topic modeling. As a result, concepts proposed in an international organization such as the United Nations (UN) have been discussed in academia, and the media have formed a variety of agendas.

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APA Style
Lee, H., Lee, S.H., Lee, K.R., Kim, J.H. (2023). ESG discourse analysis through bertopic: comparing news articles and academic papers. Computers, Materials & Continua, 75(3), 6023-6037. https://doi.org/10.32604/cmc.2023.039104
Vancouver Style
Lee H, Lee SH, Lee KR, Kim JH. ESG discourse analysis through bertopic: comparing news articles and academic papers. Comput Mater Contin. 2023;75(3):6023-6037 https://doi.org/10.32604/cmc.2023.039104
IEEE Style
H. Lee, S.H. Lee, K.R. Lee, and J.H. Kim "ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers," Comput. Mater. Contin., vol. 75, no. 3, pp. 6023-6037. 2023. https://doi.org/10.32604/cmc.2023.039104



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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