We source our ESG data from company filings where we apply NLP to extract those keywords: -Social = 'Accident, (Adult AND entertainment), Alcohol, Anti-personnel, Behavior, Charity, (Child AND Labor), Community, Controversial, Controversy, Discrimination, Gambling, Health, Human capital, Human rights, Inclusion, Injury, Labor, Munitions, Opposition, Pay, Philanthropic, Quality, Responsible.' -Environment = 'Biodiversity, Carbon, Cleantech, Clean, Climate, Coal, Conservation, Ecosystem, Emission, Energy, Fuel, Green, Land, Natural, Pollution, (Raw AND materials), Renewable, Resources, Sustainability, Sustainable, Toxic, Waste, Water.' -Governance = 'Advocacy, Bribery, Compensation, Competitive, Corruption, (Data AND breach), Divestment, Fraud, (Global AND Compact), GRI, (Global AND Reporting AND Initiative), Independent, Justice, Stability, Stewardship, Transparency.' Those are the key concepts that we extract from the statements to create the score. We then, with NLP, create the sentiment score to finally create a ranking score. We have developed our methodology based on available frameworks. We also compare each company to the industry benchmark to be able to rank ESG companies compared to their peers. For the bounds of the rating, a score of 0 is the worst and 100 is the best, so a 50 would be average. You can read more here https://site.financialmodelingprep.com/developer/docs/esg-score-api/