{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:11:18Z","timestamp":1776273078581,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The integration of Environmental, Social and Governance (ESG) considerations into business decisions and investment strategies have accelerated over the past few years. It is important to quantify the extent to which ESG-related conversations are carried out by companies so that their impact on business operations can be objectively assessed. However, profiling ESG language is challenging due to its multi-faceted nature and the lack of supervised datasets. This research study aims to detect historical trends in ESG discussions by analyzing the transcripts of corporate earning calls. The proposed solution exploits recent advances in neural language modeling to understand the linguistic structure in ESG discourse. In detail, firstly we develop a classification model that categorizes the relevance of a text sentence to ESG. A pre-trained language model is fine-tuned on a small corporate sustainability reports dataset for this purpose. The semantic knowledge encoded in this classification model is then leveraged by applying it to the sentences in the conference transcripts using a novel distant-supervision approach. Extensive empirical evaluations against various pretraining techniques demonstrate the efficacy of the proposed transfer learning framework. Our analysis indicates that in the last 5 years, nearly 15% of the discussions during earnings calls pertained to ESG, implying that ESG factors are integral to business strategy.<\/jats:p>","DOI":"10.3390\/make2040025","type":"journal-article","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T10:14:22Z","timestamp":1603275262000},"page":"453-468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Mapping ESG Trends by Distant Supervision of Neural Language Models"],"prefix":"10.3390","volume":"2","author":[{"given":"Natraj","family":"Raman","sequence":"first","affiliation":[{"name":"J.P. Morgan AI Research, London E14 5JP, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grace","family":"Bang","sequence":"additional","affiliation":[{"name":"Bloomberg LP, New York, NY 10017, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armineh","family":"Nourbakhsh","sequence":"additional","affiliation":[{"name":"J.P. Morgan AI Research, New York, NY 10179, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"ref_1","unstructured":"Vincent, O.M. (2012). The Impact of Corporate Environmental Responsibility on Financial Performance: Perspective From the Multinational Extractive Sector. [Ph.D. Thesis, Brunel University]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1080\/20430795.2015.1118917","article-title":"ESG and financial performance: Aggregated evidence from more than 2000 empirical studies","volume":"5","author":"Friede","year":"2015","journal-title":"J. Sustain. Financ. Investig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1080\/00036840500428112","article-title":"The impact of Social Responsibility on productivity and efficiency of US listed companies","volume":"40","author":"Becchetti","year":"2007","journal-title":"Appl. Econ."},{"key":"ref_4","unstructured":"Avlonas, N. (2020, October 07). Sustainability Reporting Trends in North America. Available online: https:\/\/www.cse-net.org\/wp-content\/uploads\/documents\/Sustainability-Reporting-Trends-in-North%20America%20_RS.pdf."},{"key":"ref_5","unstructured":"Kwon, S. (2020, October 07). State of Sustainability and Integrated Reporting 2018. Available online: https:\/\/corpgov.law.harvard.edu\/2018\/12\/03\/state-of-integrated-and-sustainability-reporting-2018\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1450006","DOI":"10.1142\/S1469026814500060","article-title":"Automatic analysis of corporate sustainability reports and intelligent scoring","volume":"13","author":"Shahi","year":"2014","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15900","DOI":"10.3390\/su71215791","article-title":"Managing Nature\u2013Business as Usual: Resource Extraction Companies and Their Representations of Natural Landscapes","volume":"7","author":"Brown","year":"2015","journal-title":"Sustainability"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, W.Y., and Hua, Z. (2014, January 23\u201325). A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland.","DOI":"10.3115\/v1\/P14-1109"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Keith, K., and Stent, A. (2019). Modeling Financial Analysts\u2019 Decision Making via the Pragmatics and Semantics of Earnings Calls. arXiv, Available online: https:\/\/arxiv.org\/abs\/1906.02868.","DOI":"10.18653\/v1\/P19-1047"},{"key":"ref_10","unstructured":"Qin, Y., and Yang, Y. (August, January 28). What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_11","unstructured":"Napier, E. (2019). Technology Enabled Social Responsibility Projects and an Empirical Test of CSR\u2019s Impact on Firm Performance. [Ph.D. Thesis, Georgia State University]."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00295-9","article-title":"Prediction of ESG compliance using a heterogeneous information network","volume":"7","author":"Hisano","year":"2020","journal-title":"J. Big Data"},{"key":"ref_13","unstructured":"Nematzadeh, A., Bang, G., Liu, X., and Ma, Z. (2019). Empirical Study on Detecting Controversy in Social Media. arXiv."},{"key":"ref_14","unstructured":"Ribando, J.M., and Bonne, G. (2010). A New Quality Factor: Finding Alpha With ASSET4 ESG Data, Thomson Reuters."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2388","DOI":"10.1016\/j.jbankfin.2011.02.007","article-title":"Does corporate social responsibility affect the cost of capital?","volume":"35","author":"Guedhami","year":"2011","journal-title":"J. Bank. Financ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/0015198X.2019.1654299","article-title":"Corporate Governance, ESG, and Stock Returns around the World","volume":"75","author":"Khan","year":"2019","journal-title":"Financ. Anal. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1093\/rfs\/hhz137","article-title":"The importance of climate risks for institutional investors","volume":"33","author":"Krueger","year":"2020","journal-title":"Rev. Financ. Stud."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, T., Jamet, N., Betrix, V., Piquet, L.A., and Hauptmann, E. (2020). ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction. arXiv.","DOI":"10.2139\/ssrn.3593885"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_20","unstructured":"Goel, T., Jain, P., Verma, I., Dey, L., and Paliwal, S. (2020, July 30). Mining Company Sustainability Reports to Aid Financial Decision-Making. Available online: https:\/\/www.researchgate.net\/publication\/343305380."},{"key":"ref_21","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, NIPS."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Neumann, M., Zettlemoyer, L., and Yih, W.t. (2018). Dissecting contextual word embeddings: Architecture and representation. arXiv.","DOI":"10.18653\/v1\/D18-1179"},{"key":"ref_23","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA."},{"key":"ref_24","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., and Le, Q.V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems, NIPS."},{"key":"ref_25","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_26","unstructured":"Cisco (2019, March 15). Corporate Social Resposibility Report. Available online: https:\/\/www.cisco.com\/c\/dam\/m\/en_us\/about\/csr\/csr-report\/2019\/_pdf\/csr-report-2019.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/j.jclepro.2015.05.108","article-title":"Designing a general set of sustainability indicators at the corporate level","volume":"108","author":"Rahdari","year":"2015","journal-title":"J. Clean. Prod."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., and Funtowicz, M. (2019). HuggingFace\u2019s Transformers: State-of-the-art Natural Language Processing. arXiv.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref_29","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_30","unstructured":"Allianz (2020, February 10). Ethics and Investing: How Environmental, Social, and Governance Issues Impact Investor Behavior. Available online: https:\/\/www.allianzlife.com\/-\/media\/files\/allianz\/pdfs\/esg-white-paper.pdf."},{"key":"ref_31","unstructured":"Polk, D. (2020, February 10). UN Sustainable Development Goals\u2014The Leading ESG Framework for Public Companies?. Available online: https:\/\/www.davispolk.com\/files\/2018-09-20_un_sustainable_development_goals_the_leading_esg_framework_for_large_companies.pdf."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/4\/25\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:25:03Z","timestamp":1760178303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/2\/4\/25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,21]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["make2040025"],"URL":"https:\/\/doi.org\/10.3390\/make2040025","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,21]]}}}