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Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI

Author

Listed:
  • Aradhana Saxena

    (Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India)

  • A. Santhanavijayan

    (Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli 620015, Tamil Nadu, India)

  • Harish Kumar Shakya

    (Department of AIML, Manipal University Jaipur, Jaipur 303007, Rajasthan, India)

  • Gyanendra Kumar

    (Department of IoT and Intelligent Systems, Manipal University Jaipur, Jaipur 303007, Rajasthan, India)

  • Balamurugan Balusamy

    (Office of Dean of Academics, Shiv Nadar University, Delhi-NCR Campus, Noida 201305, Uttar Pradesh, India)

  • Francesco Benedetto

    (Economics Department, University of ROMA TRE, Via Silvio D’Amico 77, 00145 Rome, Italy)

Abstract

In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced by existing research, it is regarded as the dominant component within ESG. In this study, the ESG score is derived primarily from the environmental score. The increasing importance of the environmental, social, and governance (ESG) factors in financial markets, along with the growing need for sentiment analysis in sustainability, has necessitated the development of advanced sentiment analysis techniques. A predictive model has been introduced utilizing a nested sentiment analysis framework, which classifies sentiments towards eco-friendly and non-eco-friendly products, as well as positive and negative sentiments, using FinBERT. The model has been optimized with the AdamW optimizer, L2 regularization, and dropout to assess how sentiments related to these product types influence ESG metrics. The “black-box” nature of the model has been addressed through the application of explainable AI (XAI) to enhance its interpretability. The model demonstrated an accuracy of 91.76% in predicting ESG scores and 99% in sentiment classification. The integration of XAI improves the transparency of the model’s predictions, making it a valuable tool for decision-making in making sustainable investments. This research is aligned with the United Nations’ Sustainable Development Goals (SDG 12 and SDG 13), contributing to the promotion of sustainable practices and fostering improved market dynamics.

Suggested Citation

  • Aradhana Saxena & A. Santhanavijayan & Harish Kumar Shakya & Gyanendra Kumar & Balamurugan Balusamy & Francesco Benedetto, 2024. "Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI," Mathematics, MDPI, vol. 12(21), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3332-:d:1505175
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    References listed on IDEAS

    as
    1. Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
    2. Mark Anthony Camilleri & Livio Cricelli & Roberto Mauriello & Serena Strazzullo, 2023. "Consumer Perceptions of Sustainable Products: A Systematic Literature Review," Sustainability, MDPI, vol. 15(11), pages 1-18, June.
    3. Rusitha Wijekoon & Mohamad Fazli Sabri, 2021. "Determinants That Influence Green Product Purchase Intention and Behavior: A Literature Review and Guiding Framework," Sustainability, MDPI, vol. 13(11), pages 1-40, May.
    4. Wu, Wei & Xu, Meiqi & Su, Ruiqian & Ullah, Kaleem, 2024. "Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models," Energy, Elsevier, vol. 289(C).
    5. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
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