| Issue |
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence, Machine Learning & Data Analytics | |
| DOI | https://doi.org/10.1051/e3sconf/202672301003 | |
| Published online | 08 July 2026 | |
Machine Learning–Based Classification of ESG News: Environmental Information from Digital Media in Vietnam
1 School of Industrial Management, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, Ho Chi Minh City, Vietnam. e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
2 Faculty of Business Administration, Van Lang University, Ho Chi Minh City, Vietnam e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
3 Faculty of Accounting & Auditing, Van Lang University, Ho Chi Minh City, Vietnam e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
* Corresponding author: e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Vietnam’s ESG disclosure landscape remains fragmented and report-based ESG data is scarce, leaving stakeholders with limited tools to monitor corporate environmental behaviour in near real time. We address this gap by developing an end-to-end pipeline that classifies Vietnamese ESG news on the environmental (E) pillar and links each classification to the Global Reporting Initiative (GRI) framework through a ChatGPT-based interpretability agent (“ClearESG-E”). The pipeline is benchmarked on a manually labelled corpus of news articles covering firms in the Vietnam Sustainability Index (VNSI) and externally validated on a disjoint set of newly labelled articles. The contribution is threefold: a Vietnamese ESG-news classification framework tailored to a low-resource, lexicon-driven setting; an externally validated evidence of generalisation that the top classifiers retain strong discriminative performance on unseen content; and an interpretability layer that maps each environmentally relevant article to GRI sub-criteria with traceable textual evidence, supporting transparent ESG monitoring in emerging markets.
Key words: ESG News Classification / Machine Learning / NLP / Vietnam Sustainability Index / AI Agent
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.