• DocumentCode
    2912446
  • Title

    Intelligent Corporate Sustainability report scoring solution using machine learning approach to text categorization

  • Author

    Shahi, A.M. ; Issac, Biju ; Modapothala, J.R.

  • Author_Institution
    Sch. of Eng., Swinburne Univ. of Technol., Kuching, Malaysia
  • fYear
    2012
  • fDate
    6-9 Oct. 2012
  • Firstpage
    227
  • Lastpage
    232
  • Abstract
    Development of an intelligent software system to analyze and score Corporate Sustainability reports within the Global Reporting Initiative (GRI) framework has been well foreseen and in a high demand since the latest framework´s publication in 2000´s. As the number of reporting organizations and published reports is increasing exponentially, development of a software system to automate the daunting manual scoring process seems even more vital. We describe our preliminary efforts and the related results of our efforts in building such software through application of machine learning approach to text classification. Conduction of earlier training on thousands of sample documents to construct machine learning based classifiers inductively is our primary approach to solving this problem.
  • Keywords
    learning (artificial intelligence); text analysis; GRI; global reporting initiative; intelligent corporate sustainability report scoring solution; intelligent software system; machine learning approach; text categorization; text classification; Classification algorithms; Filtering algorithms; Machine learning; Neural networks; Organizations; Text categorization; Training; CSR; GRI; Global reporting initiative; NaiveBayes; corporate sustainability report; feature selection; machine learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sustainable Utilization and Development in Engineering and Technology (STUDENT), 2012 IEEE Conference on
  • Conference_Location
    Kuala Lumpur
  • ISSN
    1985-5753
  • Print_ISBN
    978-1-4673-1649-1
  • Electronic_ISBN
    1985-5753
  • Type

    conf

  • DOI
    10.1109/STUDENT.2012.6408409
  • Filename
    6408409