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

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.