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.