IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3332-d1505175.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3332/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/21/3332/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Edin Güçlü Sözer & Mustafa Emre Civelek & Adnan Veysel Ertemel & Mustafa Çağrı Pehlivanoğlu, 2024. "The Determinants of Green Purchasing in the Hospitality Sector: A Study on the Mediation Effect of LOHAS Orientation," Sustainability, MDPI, vol. 16(23), pages 1-26, December.
    2. Sandile Mkhize & Debbie Ellis, 2024. "Organic consumption as a means to achieve sustainable development goals and agenda 2063," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 5181-5192, October.
    3. Ryan Quek Wei Heng & Edoardo Vittori & Keane Ong & Rui Mao & Erik Cambria & Gianmarco Mengaldo, 2025. "Leveraging LLMS for Top-Down Sector Allocation In Automated Trading," Papers 2503.09647, arXiv.org, revised Apr 2025.
    4. Naman Sreen & Swetarupa Chatterjee & Seema Bhardwaj & Asmita Chitnis, 2023. "Reasons and intuitions: extending behavioural reasoning theory to determine green purchase behavior," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 20(2), pages 447-475, June.
    5. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    6. Hoshiar Mal & Nagendra Singh Nehra, 2023. "The Impact of IoT Characteristics, Cultural Factors and Safety Concerns on Consumer Purchase Intention of Green Electronic Products," Sustainability, MDPI, vol. 15(8), pages 1-12, April.
    7. Vanessa Effendy & Zengrui Xiao, 2025. "How Does Cultural Sustainability Promote Fashion Consumers’ Purchase Intention?," Sustainability, MDPI, vol. 17(5), pages 1-14, February.
    8. Hiroko Oe & Yasuyuki Yamaoka & Hiroko Ochiai, 2023. "Personal and Emotional Values Embedded in Thai-Consumers’ Perceptions: Key Factors for the Sustainability of Traditional Confectionery Businesses," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    9. Julien Geissmar & Thomas Niemand & Sascha Kraus, 2023. "Surprisingly unsustainable: How and when hindsight biases shape consumer evaluations of unsustainable and sustainable products," Business Strategy and the Environment, Wiley Blackwell, vol. 32(8), pages 5969-5991, December.
    10. Shizheng Huang & Chunyuan Ke, 2024. "Research on Disruptive Green Technological Innovation in Agriculture Driven by Low-Carbon Initiatives," Sustainability, MDPI, vol. 16(24), pages 1-20, December.
    11. Serena Strazzullo & Livio Cricelli & Ciro Troise & Mark Anthony Camilleri, 2025. "Leveraging Industry 4.0 technologies for sustainable value chains: Raising awareness on digital transformation and responsible operations management," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(2), pages 2189-2202, April.
    12. Sara de Sio & Alessandra Zamagni & Giulia Casu & Paola Gremigni, 2022. "Green Trust as a Mediator in the Relationship between Green Advertising Skepticism, Environmental Knowledge, and Intention to Buy Green Food," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    13. Ting Chi & Victoria Gonzalez & Justin Janke & Mya Phan & Weronika Wojdyla, 2023. "Unveiling the Soaring Trend of Fashion Rental Services: A U.S. Consumer Perspective," Sustainability, MDPI, vol. 15(19), pages 1-21, September.
    14. Subhajit Pahari & Debarun Chakraborty & Aruna Polisetty & Ganesh Dash & Mark Anthony Camilleri & Justin Zhang, 2024. "Factors affecting consumer purchases of natural foods: Prioritizing health consciousness and environmental sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 8247-8266, December.
    15. Ishaq, Muhammad Ishtiaq & Baloch, Rukhsar & Raza, Ali & Talpur, Qurat-ul-ain & Ahmad, Rehan, 2025. "Ecological consciousness, moral self-identity and green conspicuous behavior: Moderating role of religiosity," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    16. Yezheng Li & Pinyi Yao & Syuhaily Osman & Norzalina Zainudin & Mohamad Fazli Sabri, 2022. "A Thematic Review on Using Food Delivery Services during the Pandemic: Insights for the Post-COVID-19 Era," IJERPH, MDPI, vol. 19(22), pages 1-22, November.
    17. Akanksha Saini & Abhishek Kumar & Saroj Kumar Mishra & Sanjay Kumar Kar & Rohit Bansal, 2024. "Do environment-friendly toys have a future? An empirical assessment of buyers' green toys decision-making," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(3), pages 5869-5889, March.
    18. Pinyi Yao & Syuhaily Osman & Mohamad Fazli Sabri & Norzalina Zainudin, 2022. "Consumer Behavior in Online-to-Offline (O2O) Commerce: A Thematic Review," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    19. Moon-Yong Kim & Minhee Son, 2021. "What Determines Consumer Attitude toward Green Credit Card Services? A Moderated Mediation Approach," Sustainability, MDPI, vol. 13(19), pages 1-18, September.
    20. Yanzhao Zou & Dorien Herremans, 2022. "PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin," Papers 2206.00648, arXiv.org, revised Oct 2023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3332-:d:1505175. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.