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

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.