Using Machine Learning to Generate a Dictionary for Environmental Issues

Daniel E. O’Leary; Yangin Yoon

doi:10.1007/978-3-031-40837-3_9

Using Machine Learning to Generate a Dictionary for Environmental Issues

Daniel E. O’Leary, Yangin Yoon

Dept. of Industrial Engineering

University of Southern California

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The purpose of this paper is to investigate the use of machine learning approaches to build a dictionary of terms to analyze text for ESG content using a bag of words approach, where ESG stands for “environment, social and governance.” Specifically, the paper reviews some experiments performed to develop a dictionary for information about the environment, for “carbon footprint”. We investigate using Word2Vec based on Form 10K text and from Earnings Calls, and queries of ChatGPT and compare the results. As part of the development of our dictionaries we find that bigrams and trigrams are more likely to be found when using ChatGPT, suggesting that bigrams and trigrams provide a “better” approach for the dictionaries developed with Word2Vec. We also find that terms provided by ChatGPT were not as likely to appear in Form 10Ks or other business disclosures, as were those terms generated using Word2Vec. In addition, we explored different question approaches to ChatGPT to find different perspectives on carbon footprint, such as “reducing carbon footprint” or “negative effects of carbon footprint.” We then discuss combining the findings from each of these approaches, to build a dictionary that could be used alone or with other ESG concept dictionaries.

Original language	English
Title of host publication	Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings
Editors	Andreas Holzinger, Andreas Holzinger, Andreas Holzinger, Peter Kieseberg, Federico Cabitza, Andrea Campagner, A Min Tjoa, Edgar Weippl, Edgar Weippl
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	141-154
Number of pages	14
ISBN (Print)	9783031408366
DOIs	https://doi.org/10.1007/978-3-031-40837-3_9
State	Published - 2023
Event	Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 - Benevento, Italy Duration: 28 Aug 2023 → 1 Sep 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14065 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023
Country/Territory	Italy
City	Benevento
Period	28/08/23 → 1/09/23

Keywords

Bag of Words
Carbon Footprint
ChatGPT
Concept
Dictionary
ESG
Environment
Form 10K
Hybrid Approach
Ontology
Reducing Carbon Footprint
Word2Vec

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-40837-3_9

Cite this

O’Leary, D. E., & Yoon, Y. (2023). Using Machine Learning to Generate a Dictionary for Environmental Issues. In A. Holzinger, A. Holzinger, A. Holzinger, P. Kieseberg, F. Cabitza, A. Campagner, A. M. Tjoa, E. Weippl, & E. Weippl (Eds.), Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings (pp. 141-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14065 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-40837-3_9

O’Leary, Daniel E. ; Yoon, Yangin. / Using Machine Learning to Generate a Dictionary for Environmental Issues. Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings. editor / Andreas Holzinger ; Andreas Holzinger ; Andreas Holzinger ; Peter Kieseberg ; Federico Cabitza ; Andrea Campagner ; A Min Tjoa ; Edgar Weippl ; Edgar Weippl. Springer Science and Business Media Deutschland GmbH, 2023. pp. 141-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{85499bfba4d146a6bf1ee779c3b9f09b,

title = "Using Machine Learning to Generate a Dictionary for Environmental Issues",

abstract = "The purpose of this paper is to investigate the use of machine learning approaches to build a dictionary of terms to analyze text for ESG content using a bag of words approach, where ESG stands for “environment, social and governance.” Specifically, the paper reviews some experiments performed to develop a dictionary for information about the environment, for “carbon footprint”. We investigate using Word2Vec based on Form 10K text and from Earnings Calls, and queries of ChatGPT and compare the results. As part of the development of our dictionaries we find that bigrams and trigrams are more likely to be found when using ChatGPT, suggesting that bigrams and trigrams provide a “better” approach for the dictionaries developed with Word2Vec. We also find that terms provided by ChatGPT were not as likely to appear in Form 10Ks or other business disclosures, as were those terms generated using Word2Vec. In addition, we explored different question approaches to ChatGPT to find different perspectives on carbon footprint, such as “reducing carbon footprint” or “negative effects of carbon footprint.” We then discuss combining the findings from each of these approaches, to build a dictionary that could be used alone or with other ESG concept dictionaries.",

keywords = "Bag of Words, Carbon Footprint, ChatGPT, Concept, Dictionary, ESG, Environment, Form 10K, Hybrid Approach, Ontology, Reducing Carbon Footprint, Word2Vec",

author = "O{\textquoteright}Leary, \{Daniel E.\} and Yangin Yoon",

note = "Publisher Copyright: {\textcopyright} 2023, IFIP International Federation for Information Processing.; Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023 ; Conference date: 28-08-2023 Through 01-09-2023",

year = "2023",

doi = "10.1007/978-3-031-40837-3\_9",

language = "English",

isbn = "9783031408366",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "141--154",

editor = "Andreas Holzinger and Andreas Holzinger and Andreas Holzinger and Peter Kieseberg and Federico Cabitza and Andrea Campagner and Tjoa, \{A Min\} and Edgar Weippl and Edgar Weippl",

booktitle = "Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings",

}

O’Leary, DE & Yoon, Y 2023, Using Machine Learning to Generate a Dictionary for Environmental Issues. in A Holzinger, A Holzinger, A Holzinger, P Kieseberg, F Cabitza, A Campagner, AM Tjoa, E Weippl & E Weippl (eds), Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14065 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 141-154, Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, 28/08/23. https://doi.org/10.1007/978-3-031-40837-3_9

Using Machine Learning to Generate a Dictionary for Environmental Issues. / O’Leary, Daniel E.; Yoon, Yangin.
Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings. ed. / Andreas Holzinger; Andreas Holzinger; Andreas Holzinger; Peter Kieseberg; Federico Cabitza; Andrea Campagner; A Min Tjoa; Edgar Weippl; Edgar Weippl. Springer Science and Business Media Deutschland GmbH, 2023. p. 141-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14065 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Machine Learning to Generate a Dictionary for Environmental Issues

AU - O’Leary, Daniel E.

AU - Yoon, Yangin

PY - 2023

Y1 - 2023

N2 - The purpose of this paper is to investigate the use of machine learning approaches to build a dictionary of terms to analyze text for ESG content using a bag of words approach, where ESG stands for “environment, social and governance.” Specifically, the paper reviews some experiments performed to develop a dictionary for information about the environment, for “carbon footprint”. We investigate using Word2Vec based on Form 10K text and from Earnings Calls, and queries of ChatGPT and compare the results. As part of the development of our dictionaries we find that bigrams and trigrams are more likely to be found when using ChatGPT, suggesting that bigrams and trigrams provide a “better” approach for the dictionaries developed with Word2Vec. We also find that terms provided by ChatGPT were not as likely to appear in Form 10Ks or other business disclosures, as were those terms generated using Word2Vec. In addition, we explored different question approaches to ChatGPT to find different perspectives on carbon footprint, such as “reducing carbon footprint” or “negative effects of carbon footprint.” We then discuss combining the findings from each of these approaches, to build a dictionary that could be used alone or with other ESG concept dictionaries.

AB - The purpose of this paper is to investigate the use of machine learning approaches to build a dictionary of terms to analyze text for ESG content using a bag of words approach, where ESG stands for “environment, social and governance.” Specifically, the paper reviews some experiments performed to develop a dictionary for information about the environment, for “carbon footprint”. We investigate using Word2Vec based on Form 10K text and from Earnings Calls, and queries of ChatGPT and compare the results. As part of the development of our dictionaries we find that bigrams and trigrams are more likely to be found when using ChatGPT, suggesting that bigrams and trigrams provide a “better” approach for the dictionaries developed with Word2Vec. We also find that terms provided by ChatGPT were not as likely to appear in Form 10Ks or other business disclosures, as were those terms generated using Word2Vec. In addition, we explored different question approaches to ChatGPT to find different perspectives on carbon footprint, such as “reducing carbon footprint” or “negative effects of carbon footprint.” We then discuss combining the findings from each of these approaches, to build a dictionary that could be used alone or with other ESG concept dictionaries.

KW - Bag of Words

KW - Carbon Footprint

KW - ChatGPT

KW - Concept

KW - Dictionary

KW - ESG

KW - Environment

KW - Form 10K

KW - Hybrid Approach

KW - Ontology

KW - Reducing Carbon Footprint

KW - Word2Vec

UR - http://www.scopus.com/inward/record.url?scp=85172286254&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-40837-3_9

DO - 10.1007/978-3-031-40837-3_9

M3 - Conference contribution

AN - SCOPUS:85172286254

SN - 9783031408366

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 141

EP - 154

BT - Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings

A2 - Holzinger, Andreas

A2 - Kieseberg, Peter

A2 - Cabitza, Federico

A2 - Campagner, Andrea

A2 - Tjoa, A Min

A2 - Weippl, Edgar

PB - Springer Science and Business Media Deutschland GmbH

T2 - Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023

Y2 - 28 August 2023 through 1 September 2023

ER -

O’Leary DE, Yoon Y. Using Machine Learning to Generate a Dictionary for Environmental Issues. In Holzinger A, Holzinger A, Holzinger A, Kieseberg P, Cabitza F, Campagner A, Tjoa AM, Weippl E, Weippl E, editors, Machine Learning and Knowledge Extraction - 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 141-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-40837-3_9

Using Machine Learning to Generate a Dictionary for Environmental Issues

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this