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.
|Journal||International Journal of Computational Intelligence and Applications|
|Publication status||Published - 20 Mar 2014|