Gutierrez-Bustamante, M.; Espinosa-Leal, L. Natural Language Processing Methods for Scoring Sustainability Reports—A Study of Nordic Listed Companies. Sustainability2022, 14, 9165.
Gutierrez-Bustamante, M.; Espinosa-Leal, L. Natural Language Processing Methods for Scoring Sustainability Reports—A Study of Nordic Listed Companies. Sustainability 2022, 14, 9165.
Gutierrez-Bustamante, M.; Espinosa-Leal, L. Natural Language Processing Methods for Scoring Sustainability Reports—A Study of Nordic Listed Companies. Sustainability2022, 14, 9165.
Gutierrez-Bustamante, M.; Espinosa-Leal, L. Natural Language Processing Methods for Scoring Sustainability Reports—A Study of Nordic Listed Companies. Sustainability 2022, 14, 9165.
Abstract
This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. 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 strong impact on the environment.
Keywords
Text mining; natural language processing; sustainability; semantic similarity; corporate social responsibility; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
