Analysis of Supervised Text Classification Algorithms on Corporate Sustainability Reports

被引：0

作者：

Shahi, Amir Mohammad ^{[1
]}

Issac, Biju ^{[1
]}

Modapothala, Jashua Rajesh ^{[2
]}

机构：

[1] Swinburne Univ Technol, Sch Engn Comp & Sci, Sarawak Campus, Kuching, Malaysia

[2] Swinburne Univ Technol, Sch Business & Design, Kuching, Malaysia

来源：

2011 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), VOLS 1-4 | 2012年

关键词：

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

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：96 / 100

页数：5

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