Analysis of supervised text classification algorithms on corporate sustainability reports

Hdl Handle:
http://hdl.handle.net/10149/249797
Title:
Analysis of supervised text classification algorithms on corporate sustainability reports
Book Title:
Proceedings of IEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011)
Authors:
Shahi, A. M. (Amir); Issac, B. (Biju); Modapothala, J. R. (Jashua)
Affiliation:
Swinburne University of Technology
Citation:
Shahi, A.M., Issac, B. and Modapothala, J.R. (2011) Analysis of supervised text classification algorithms on corporate sustainability reports, Proceedings of IEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011), pp.96-100.
Publisher:
IEEE
Conference:
IEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011), Harbin, China, 24-26 December 2011.
Issue Date:
24-Dec-2011
URI:
http://hdl.handle.net/10149/249797
DOI:
10.1109/ICCSNT.2011.6181917
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=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.
Type:
Meetings and Proceedings
Language:
en
Keywords:
corporate sustainability report; document categorization; feature selection; machine learning; text classification; supervised learning
ISBN:
9781457715846
Citation Count:
0 [Scopus, 22/10/2012]

Full metadata record

DC FieldValue Language
dc.contributor.authorShahi, A. M. (Amir)en_GB
dc.contributor.authorIssac, B. (Biju)en_GB
dc.contributor.authorModapothala, J. R. (Jashua)en_GB
dc.date.accessioned2012-10-22T16:15:03Zen
dc.date.available2012-10-22T16:15:03Zen
dc.date.issued2011-12-24en
dc.identifier.isbn9781457715846en
dc.identifier.doi10.1109/ICCSNT.2011.6181917en
dc.identifier.urihttp://hdl.handle.net/10149/249797en
dc.description.abstractMachine 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.en_GB
dc.language.isoenen
dc.publisherIEEEen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6181917en_GB
dc.subjectcorporate sustainability reporten_GB
dc.subjectdocument categorizationen_GB
dc.subjectfeature selectionen_GB
dc.subjectmachine learningen_GB
dc.subjecttext classificationen_GB
dc.subjectsupervised learningen_GB
dc.titleAnalysis of supervised text classification algorithms on corporate sustainability reportsen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentSwinburne University of Technologyen_GB
dc.title.bookProceedings of IEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011)en_GB
dc.identifier.conferenceIEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011), Harbin, China, 24-26 December 2011.en_GB
ref.citationcount0 [Scopus, 22/10/2012]en_GB
or.citation.harvardShahi, A.M., Issac, B. and Modapothala, J.R. (2011) Analysis of supervised text classification algorithms on corporate sustainability reports, Proceedings of IEEE International Conference on Computer Science and Network Technology 2011 (ICCSNT 2011), pp.96-100.en_GB
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