1 research outputs found
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
Context: Sustainable corporate behavior is increasingly valued by society and
impacts corporate reputation and customer trust. Hence, companies regularly
publish sustainability reports to shed light on their impact on environmental,
social, and governance (ESG) factors. Problem: Sustainability reports are
written by companies themselves and are therefore considered a
company-controlled source. Contrary, studies reveal that non-corporate channels
(e.g., media coverage) represent the main driver for ESG transparency. However,
analysing media coverage regarding ESG factors is challenging since (1) the
amount of published news articles grows daily, (2) media coverage data does not
necessarily deal with an ESG-relevant topic, meaning that it must be carefully
filtered, and (3) the majority of media coverage data is unstructured. Research
Goal: We aim to extract ESG-relevant information from textual media reactions
automatically to calculate an ESG score for a given company. Our goal is to
reduce the cost of ESG data collection and make ESG information available to
the general public. Contribution: Our contributions are three-fold: First, we
publish a corpus of 432,411 news headlines annotated as being environmental-,
governance-, social-related, or ESG-irrelevant. Second, we present our
tool-supported approach called ESG-Miner capable of analyzing and evaluating
headlines on corporate ESG-performance automatically. Third, we demonstrate the
feasibility of our approach in an experiment and apply the ESG-Miner on 3000
manually labeled headlines. Our approach processes 96.7 % of the headlines
correctly and shows a great performance in detecting environmental-related
headlines along with their correct sentiment. We encourage fellow researchers
and practitioners to use the ESG-Miner at https://www.esg-miner.com