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Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data : NLP Approach and Tool

Fischbach, Jannik (author)
Netlight Consulting GmbH, Germany
Adam, Max (author)
Technical University of Munich, Germany
Dzhagatspanyan, Victor (author)
Technical University of Munich, Germany
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Mendez, Daniel (author)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
Frattini, Julian, 1995- (author)
Blekinge Tekniska Högskola,Institutionen för programvaruteknik
Kosenkov, Oleksandr (author)
Fortiss GmbH, Germany
Elahidoost, Parisa (author)
Fortiss GmbH, Germany
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 (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2023
2023
English.
In: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350324457 ; , s. 2823-2830
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  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • [Context:] Society increasingly values sustainable corporate behaviour, impacting 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 and therefore considered a company-controlled source. Contrarily, 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 automatically extract ESG-relevant information from textual media reactions 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 automatically analysing and evaluating corporate ESG performance headlines. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labelled headlines. Our approach correctly processes 96.7% of the headlines and shows great performance in detecting environmental-related headlines and their correct sentiment. © 2023 IEEE.

Subject headings

SAMHÄLLSVETENSKAP  -- Annan samhällsvetenskap -- Tvärvetenskapliga studier inom samhällsvetenskap (hsv//swe)
SOCIAL SCIENCES  -- Other Social Sciences -- Social Sciences Interdisciplinary (hsv//eng)
SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)
SOCIAL SCIENCES  -- Economics and Business -- Business Administration (hsv//eng)

Keyword

Corporate Social Responsibility
ESG Assessment
Natural Language Processing
Social Media Mining

Publication and Content Type

ref (subject category)
kon (subject category)

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