Natural Language Processing Methods for Scoring Sustainability Reports—A Study of Nordic Listed Companies

Marcelo Gutierrez-Bustamante and Leonardo Espinosa-Leal
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Marcelo Gutierrez-Bustamante: SUSTEX.io, AI Research, 00150 Helsinki, Finland
Leonardo Espinosa-Leal: Department of Business Management and Analytics, Arcada University of Applied Sciences, 00560 Helsinki, Finland

Sustainability, 2022, vol. 14, issue 15, 1-26

Abstract: This paper aims to evaluate the degree of affinity that Nordic companies’ reports published under the Global Reporting Initiatives (GRI) framework have. Several natural language processing and text-mining techniques were implemented and tested to achieve this goal. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for word representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a substantial impact on the environment.

Keywords: text mining; natural language processing; sustainability; semantic similarity; corporate social responsibility; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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