Analysis of supervised text classification algorithms on corporate sustainability reports

Amir Mohammad Shahi; Biju Issac; Jashua Rajesh Modapothala

doi:10.1109/ICCSNT.2011.6181917

Analysis of supervised text classification algorithms on corporate sustainability reports

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

^*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

Machine Learning approach to text classification has been the dominant method in the research and application field since it was first introduced in the 1990s. It has been proven that document classification applications based on Machine Learning produce competitive results to those based on the Knowledge Based approaches. This approach has been widely researched upon as well as applied in various applications to solve various text categorization problems. In this research we have applied such techniques in a novel effort to find out which document classification algorithms perform best on Corporate Sustainability Reports.

Original language	English
Title of host publication	Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Publisher	IEEE
Pages	96-100
Number of pages	5
Volume	1
ISBN (Electronic)	978-1-4577-1587-7
ISBN (Print)	9781457715846
DOIs	https://doi.org/10.1109/ICCSNT.2011.6181917
Publication status	Published - 12 Apr 2012
Externally published	Yes
Event	2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 - Harbin, China Duration: 24 Dec 2011 → 26 Dec 2011

Conference

Conference	2011 International Conference on Computer Science and Network Technology, ICCSNT 2011
Country/Territory	China
City	Harbin
Period	24/12/11 → 26/12/11

Keywords

Corporate Sustainability Report
Document Categorization
Feature Selection
GRI
Machine Learning
Supervised Learning
Text Classification

UN SDGs

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

Access to Document

10.1109/ICCSNT.2011.6181917

Cite this

@inproceedings{36d7f214a78d43b3a65de54c886ffd8d,

title = "Analysis of supervised text classification algorithms on corporate sustainability reports",

abstract = "Machine Learning approach to text classification has been the dominant method in the research and application field since it was first introduced in the 1990s. It has been proven that document classification applications based on Machine Learning produce competitive results to those based on the Knowledge Based approaches. This approach has been widely researched upon as well as applied in various applications to solve various text categorization problems. In this research we have applied such techniques in a novel effort to find out which document classification algorithms perform best on Corporate Sustainability Reports.",

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}",

year = "2012",

month = apr,

day = "12",

doi = "10.1109/ICCSNT.2011.6181917",

language = "English",

isbn = "9781457715846",

volume = "1",

pages = "96--100",

booktitle = "Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011",

publisher = "IEEE",

address = "United States",

note = "2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 ; Conference date: 24-12-2011 Through 26-12-2011",

}

Shahi, AM, Issac, B & Modapothala, JR 2012, Analysis of supervised text classification algorithms on corporate sustainability reports. in Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011. vol. 1, 6181917, IEEE, pp. 96-100, 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011, Harbin, China, 24/12/11. https://doi.org/10.1109/ICCSNT.2011.6181917

Analysis of supervised text classification algorithms on corporate sustainability reports. / Shahi, Amir Mohammad; Issac, Biju; Modapothala, Jashua Rajesh.
Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011. Vol. 1 IEEE, 2012. p. 96-100 6181917.

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

TY - GEN

T1 - Analysis of supervised text classification algorithms on corporate sustainability reports

AU - Shahi, Amir Mohammad

AU - Issac, Biju

AU - Modapothala, Jashua Rajesh

PY - 2012/4/12

Y1 - 2012/4/12

N2 - Machine Learning approach to text classification has been the dominant method in the research and application field since it was first introduced in the 1990s. It has been proven that document classification applications based on Machine Learning produce competitive results to those based on the Knowledge Based approaches. This approach has been widely researched upon as well as applied in various applications to solve various text categorization problems. In this research we have applied such techniques in a novel effort to find out which document classification algorithms perform best on Corporate Sustainability Reports.

AB - Machine Learning approach to text classification has been the dominant method in the research and application field since it was first introduced in the 1990s. It has been proven that document classification applications based on Machine Learning produce competitive results to those based on the Knowledge Based approaches. This approach has been widely researched upon as well as applied in various applications to solve various text categorization problems. In this research we have applied such techniques in a novel effort to find out which document classification algorithms perform best on Corporate Sustainability Reports.

KW - Corporate Sustainability Report

KW - Document Categorization

KW - Feature Selection

KW - GRI

KW - Machine Learning

KW - Supervised Learning

KW - Text Classification

U2 - 10.1109/ICCSNT.2011.6181917

DO - 10.1109/ICCSNT.2011.6181917

M3 - Conference contribution

AN - SCOPUS:84860437597

SN - 9781457715846

VL - 1

SP - 96

EP - 100

BT - Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011

PB - IEEE

T2 - 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011

Y2 - 24 December 2011 through 26 December 2011

ER -

Analysis of supervised text classification algorithms on corporate sustainability reports

Abstract

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this