Analysis of supervised text classification algorithms on corporate sustainability reports

Shahi, Amir Mohammad, Issac, Biju and Modapothala, Jashua (2012) Analysis of supervised text classification algorithms on corporate sustainability reports. In: Proceedings of 2011 International Conference on Computer Science and Network Technology. IEEE, pp. 96-100. ISBN 978-1-4577-1586-0

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ICCSNT.2011.6181917

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.

Item Type: Book Section
Uncontrolled Keywords: Machine Learning, Feature Selection, Text Classification, Document Categorization, Supervised Learning, Corporate Sustainability Report, GRI
Subjects: G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 18 Dec 2018 12:05
Last Modified: 18 Dec 2018 12:05
URI: http://nrl.northumbria.ac.uk/id/eprint/37324

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence