[White Paper] A Guide to Converting Unstructured Data into Actionable ESG Scoring

Unstructured data sources, such as news articles, public filings, regulatory content, blogs and other textual content can provide an important view of how a company adheres to Environmental, Social and Governance (ESG) standards.

In fact, it represents what the world thinks of a company’s ESG performance vs. what a company may report about its own performance. Developing a Natural Language model to identify ESG content and events is an essential step which requires a collaborative effort across various specialties. Though this can be time consuming and tricky, it is a must-have in today’s world of exponential emerging data. Together with sentiment scoring and ESG event detection, ESG scores can be derived from unstructured data on a real-time basis, as information emerges.

Download this guide for the key lessons learned by Bitvore and for guidance on how to create and tune a Natural Language model to detect ESG events from unstructured data sets.