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Web of Proceedings - Francis Academic Press
Web of Proceedings - Francis Academic Press

ESG Reports Using Natural Language Processing

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DOI: 10.25236/icceme.2023.019


Yuhe Jiang, Jiali Qiu

Corresponding Author

Yuhe Jiang


In this study, we examined a lot of information about ESG and analyzed ESG factors using the Latent Dirichlet Allocation (LDA) method. This article proposes the use of natural language processing (NLP) techniques to automatically analyze ESG reports. NLP is a branch of artificial intelligence that enables computers to understand and process human language. By applying NLP to ESG reports, we can extract relevant information, identify key themes, and analyze sentiment to gain a comprehensive understanding of a company's ESG performance. The effectiveness of using NLP techniques to analyze ESG reports is demonstrated by automating the analysis process. We can gain a deeper understanding of a company's sustainability practices and decisions, and have the potential to revolutionize the analysis of ESG reports, enabling investors to more efficiently and accurately assess a company's sustainability practices.


ESG, Citi Bank, LDA, NLP