arXiv:2509.19833v4 Announce Type: replace-cross Abstract: The United Nations' Sustainable Development Goals (SDGs) provide a globally recognised framework for addressing major societal, environmental, and economic challenges. While recent advances in natural language processing (NLP) and large language models (LLMs) have enabled the automatic identification of SDG-related content, they do not capture whether the described events represent progress toward or regression from a specific goal. To address this gap, we introduce the novel task of SDG polarity detection and present SDG-POD, a benchma
The proliferation of advanced NLP and LLMs has created the technical capacity to move beyond mere identification of SDG content to nuanced sentiment analysis, which is crucial for effective sustainability tracking.
Accurate polarity detection in SDG-related texts allows for a more granular understanding of progress and regressions toward global sustainability goals, enabling more targeted interventions and policy adjustments.
The ability to automatically assess not just what is related to SDGs but whether it's positive or negative news fundamentally changes how governments, NGOs, and corporations can monitor and report on sustainability efforts.
- · UN & International Organizations
- · ESG Investors
- · Sustainability Reporting Software
- · NLP/LLM Developers
- · Manual Sustainability Auditors
- · Organizations with Lackluster SDG Performance
Automated, real-time assessment of SDG impact from news and public discourse becomes feasible.
Public and private sector entities face increased accountability from stakeholders able to track their SDG contributions more precisely.
This granular data could influence capital allocation, regulatory frameworks, and public opinion on environmental and social governance at an unprecedented scale.
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