Reliability assessment of an intelligent approach to corporate sustainability report analysis

Amir Mohammad Shahi; Biju Issac; Jashua Rajesh Modapothala

doi:10.1007/978-3-319-06773-5_31

Reliability assessment of an intelligent approach to corporate sustainability report analysis

Amir Mohammad Shahi^*, Biju Issac, Jashua Rajesh Modapothala

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper describes our efforts in developing intelligent corporate sustainability report analysis software based on machine learning approach to text categorization and illustrates the results of executing it on real-world reports to determine the reliability of applying such approach. The document ultimately aims at proving that given sufficient training and tuning, intelligent report analysis could at last replace manual methods to bring about drastic improvements in efficiency, effectiveness and capacity.

Original language	English
Title of host publication	Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering
Publisher	Springer
Pages	233-240
Number of pages	8
Volume	313
ISBN (Electronic)	978-3-319-06773-5
ISBN (Print)	978-3-319-06772-8
DOIs	https://doi.org/10.1007/978-3-319-06773-5_31
Publication status	Published - 2015
Externally published	Yes

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
ISSN (Print)	1876-1100

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

Corporate sustainability report
Document categorization
Feature selection
GRI
Machine learning
Supervised learning
Text classification

Access to Document

10.1007/978-3-319-06773-5_31

Cite this

Shahi, A. M., Issac, B., & Modapothala, J. R. (2015). Reliability assessment of an intelligent approach to corporate sustainability report analysis. In Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering (Vol. 313, pp. 233-240). (Lecture Notes in Electrical Engineering). Springer. https://doi.org/10.1007/978-3-319-06773-5_31

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abstract = "This paper describes our efforts in developing intelligent corporate sustainability report analysis software based on machine learning approach to text categorization and illustrates the results of executing it on real-world reports to determine the reliability of applying such approach. The document ultimately aims at proving that given sufficient training and tuning, intelligent report analysis could at last replace manual methods to bring about drastic improvements in efficiency, effectiveness and capacity.",

keywords = "Corporate sustainability report, Document categorization, Feature selection, GRI, Machine learning, Supervised learning, Text classification",

author = "Shahi, \{Amir Mohammad\} and Biju Issac and Modapothala, \{Jashua Rajesh\}",

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language = "English",

isbn = "978-3-319-06772-8",

volume = "313",

series = "Lecture Notes in Electrical Engineering",

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pages = "233--240",

booktitle = "Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering",

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Reliability assessment of an intelligent approach to corporate sustainability report analysis. / Shahi, Amir Mohammad; Issac, Biju; Modapothala, Jashua Rajesh.
Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Vol. 313 Springer, 2015. p. 233-240 (Lecture Notes in Electrical Engineering).

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

TY - CHAP

T1 - Reliability assessment of an intelligent approach to corporate sustainability report analysis

AU - Shahi, Amir Mohammad

AU - Issac, Biju

AU - Modapothala, Jashua Rajesh

PY - 2015

Y1 - 2015

N2 - This paper describes our efforts in developing intelligent corporate sustainability report analysis software based on machine learning approach to text categorization and illustrates the results of executing it on real-world reports to determine the reliability of applying such approach. The document ultimately aims at proving that given sufficient training and tuning, intelligent report analysis could at last replace manual methods to bring about drastic improvements in efficiency, effectiveness and capacity.

AB - This paper describes our efforts in developing intelligent corporate sustainability report analysis software based on machine learning approach to text categorization and illustrates the results of executing it on real-world reports to determine the reliability of applying such approach. The document ultimately aims at proving that given sufficient training and tuning, intelligent report analysis could at last replace manual methods to bring about drastic improvements in efficiency, effectiveness and capacity.

KW - Corporate sustainability report

KW - Document categorization

KW - Feature selection

KW - GRI

KW - Machine learning

KW - Supervised learning

KW - Text classification

UR - https://www.scopus.com/pages/publications/84921934365

U2 - 10.1007/978-3-319-06773-5_31

DO - 10.1007/978-3-319-06773-5_31

M3 - Chapter

AN - SCOPUS:84921934365

SN - 978-3-319-06772-8

VL - 313

T3 - Lecture Notes in Electrical Engineering

SP - 233

EP - 240

BT - Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering

PB - Springer

ER -

Reliability assessment of an intelligent approach to corporate sustainability report analysis

Abstract

Publication series

UN SDGs

Keywords

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