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Understanding Public Opinion towards ESG and Green Finance with the Use of Explainable Artificial Intelligence

Author

Listed:
  • Wihan van der Heever

    (College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

  • Ranjan Satapathy

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Ji Min Park

    (Nanyang Business School, Nanyang Technological University, Gaia, 91 Nanyang Avenue, Singapore 639956, Singapore)

  • Erik Cambria

    (College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

Abstract

This study leverages explainable artificial intelligence (XAI) techniques to analyze public sentiment towards Environmental, Social, and Governance (ESG) factors, climate change, and green finance. It does so by developing a novel multi-task learning framework combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning to extract nuanced insights from a large corpus of social media data. Our approach integrates state-of-the-art models, including the SenticNet API, for sentiment analysis and implements multiple XAI methods such as LIME, SHAP, and Permutation Importance to enhance interpretability. Results reveal predominantly positive sentiment towards environmental topics, with notable variations across ESG categories. The contrastive learning visualization demonstrates clear sentiment clustering while highlighting areas of uncertainty. This research contributes to the field by providing an interpretable, trustworthy AI system for ESG sentiment analysis, offering valuable insights for policymakers and business stakeholders navigating the complex landscape of sustainable finance and climate action. The methodology proposed in this paper advances the current state of AI in ESG and green finance in several ways. By combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning, our approach provides a more comprehensive understanding of public sentiment towards ESG factors than traditional methods. The integration of multiple XAI techniques (LIME, SHAP, and Permutation Importance) offers a transparent view of the subtlety of the model’s decision-making process, which is crucial for building trust in AI-driven ESG assessments. Our approach enables a more accurate representation of public opinion, essential for informed decision-making in sustainable finance. This paper paves the way for more transparent and explainable AI applications in critical domains like ESG.

Suggested Citation

  • Wihan van der Heever & Ranjan Satapathy & Ji Min Park & Erik Cambria, 2024. "Understanding Public Opinion towards ESG and Green Finance with the Use of Explainable Artificial Intelligence," Mathematics, MDPI, vol. 12(19), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3119-:d:1492705
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    References listed on IDEAS

    as
    1. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    2. Samuel Drempetic & Christian Klein & Bernhard Zwergel, 2020. "The Influence of Firm Size on the ESG Score: Corporate Sustainability Ratings Under Review," Journal of Business Ethics, Springer, vol. 167(2), pages 333-360, November.
    3. Caroline Flammer & Michael W. Toffel & Kala Viswanathan, 2021. "Shareholder activism and firms' voluntary disclosure of climate change risks," Strategic Management Journal, Wiley Blackwell, vol. 42(10), pages 1850-1879, October.
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