Automatic analysis of corporate sustainability reports and intelligent scoring

Shahi, Amir, Issac, Biju and Modapothala, Jashua (2014) Automatic analysis of corporate sustainability reports and intelligent scoring. International Journal of Computational Intelligence and Applications, 13 (01). p. 1450006. ISSN 1469-0268

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Official URL: http://dx.doi.org/10.1142/S1469026814500060

Abstract

As more and more corporations and business entities have been publishing corporate sustainability reports, the current manual process of analyzing the reports is becoming obsolete and tedious. Development of an intelligent software tool to perform the report analysis task would be an ideal solution to this long standing problem. In this paper we argue that, given sufficient quality training using a custom corpus, corporate sustainability reports can be analyzed in mass numbers using a supervised learning based text mining software. We also discuss our methodologies of improving the accuracy of our classifier as well as the feature selector in order to gain better performance and more stability. Additionally, the achieved results of executing the developed software on one hundred reports are discussed in order to prove our claims.

Item Type: Article
Uncontrolled Keywords: Machine learning, text analysis, text mining, clustering, classification, association rules
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 24 Oct 2018 07:44
Last Modified: 11 Oct 2019 18:01
URI: http://nrl.northumbria.ac.uk/id/eprint/36413

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