Semantic clustering and affinity measure of subject-oriented language texts

Mikhailov, D. V.; Emelyanov, G. M.

doi:10.1134/s1054661810030144

Cited by 5 publications

(1 citation statement)

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“…This research aims to quantify the degree of affinity of CSR reports provided by a selected group of Nordic companies under the guidelines of the GRI Standards. The degree of affinity is obtained via a similarity measure between the numerical representations obtained by the combination of text-mining methods [9][10][11]. Therefore, this work implements natural-language-processing (NLP) and information-retrieval (IR) methods to obtain semantic similarity and matching disclosures, respectively.…”

Section: Introductionmentioning

confidence: 99%

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

Gutierrez-Bustamante

Espinosa-Leal

2022

Sustainability

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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.

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Section: Introductionmentioning

confidence: 99%