Title :
Analysis of supervised text classification algorithms on corporate sustainability reports
Author :
Shahi, Amir Mohammad ; Issac, Biju ; Modapothala, Jashua Rajesh
Author_Institution :
Sch. of Eng., Comput. & Sci., Swinburne Univ. of Technol., Kuching, Malaysia
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 :
learning (artificial intelligence); pattern classification; report generators; text analysis; corporate sustainability reports; document classification algorithm; knowledge based approach; machine learning; supervised text classification algorithm; text categorization problem; Artificial neural networks; Niobium; Corporate Sustainability Report; Document Categorization; Feature Selection; GRI; Machine Learning; Supervised Learning; Text Classification;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2011 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4577-1586-0
DOI :
10.1109/ICCSNT.2011.6181917