Meticulously Analyzing ESG Disclosure: A Data-Driven Approach.

Using NLP to analyze ESG reports has gained a lot of attention. However, existing supervised learning approaches rely on high-level and predetermined ESG topics (as used by reporting standards/rating agencies), which often fail to capture specific, latest trends and impactful issues in specific industries, while fully unsupervised approaches yield generic topics that are not useful for practical analysis. We proposed a novel data-driven and dynamic approach that base on the report contents to identify important and trendy issues that cannot be revealed by previous approaches. Technically speaking, our approach combines supervised text classification on industry-specific material topics with unsupervised topic modeling. The identified issues can be ranked using a simple word counting method. To illustrate the usefulness of our methodology, we apply it to a set of ESG reports from the banking industry. The identified issues, representing the trendy issues, can also be used to show the different priorities of focuses between banks from different regions. Time-series analysis can be done as well to see the changes in priority of issues over time. We are able to validate (indirectly and intuitively) that some of the issues should be correct, which show that our approach is promising.