Creating a Systematic ESG (Environmental Social Governance) Scoring
  System Using Social Network Analysis and Machine Learning for More
  Sustainable Company Practices
Aarav Patel Peter Gloor
Environmental Social Governance (ESG) is a widely used metric that measures
the sustainability of a company practices. Currently, ESG is determined using
self-reported corporate filings, which allows companies to portray themselves
in an artificially positive light. As a result, ESG evaluation is subjective
and inconsistent across raters, giving executives mixed signals on what to
improve. This project aims to create a data-driven ESG evaluation system that
can provide better guidance and more systemized scores by incorporating social
sentiment. Social sentiment allows for more balanced perspectives which
directly highlight public opinion, helping companies create more focused and
impactful initiatives. To build this, Python web scrapers were developed to
collect data from Wikipedia, Twitter, LinkedIn, and Google News for the S&P 500
companies. Data was then cleaned and passed through NLP algorithms to obtain
sentiment scores for ESG subcategories. Using these features, machine-learning
algorithms were trained and calibrated to S&P Global ESG Ratings to test their
predictive capabilities. The Random-Forest model was the strongest model with a
mean absolute error of 13.4% and a correlation of 26.1% (p-value 0.0372),
showing encouraging results. Overall, measuring ESG social sentiment across
sub-categories can help executives focus efforts on areas people care about
most. Furthermore, this data-driven methodology can provide ratings for
companies without coverage, allowing more socially responsible firms to thrive.