Title
Applying machine learning in accounting research Applying machine learning in accounting research
Author
Faculty/Department
Faculty of Applied Economics
Publication type
article
Publication
New York ,
Subject
Economics
Source (journal)
Expert systems with applications. - New York
Volume/pages
38(2011) :10 , p. 13414-13424
ISSN
0957-4174
ISI
000292169500153
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Quite often, in order to derive meaningful insights, accounting researchers have to analyze large bodies of text. Usually, this is done manually by several human coders, which makes the process time consuming, expensive, and often neither replicable nor accurate. In an attempt to mitigate these problems, we perform a feasibility study investigating the applicability of computer-aided content analysis techniques onto the domain of accounting research. Krippendorff (1980) defines an algorithms reliability as its stability, reproducibility and accuracy. Since in computer-aided text classification, which is inherently objective and repeatable, the first two requirements, stability and reproducibility, are not an issue, this paper focuses exclusively on the third requirement, the algorithms accuracy. It is important to note that, although inaccurate classification results are completely worthless, it is surprising to see how few research papers actually mention the accuracy of the used classification methodology. After a survey of the available techniques, we perform an in depth analysis of the most promising one, LPU (Learning from Positive and Unlabelled), which turns out to have an F-value and accuracy of about 90%, which means that, given a random text, it has a 90% probability of classifying it correctly. Highlights ► We examine text classification algorithms in an accounting setting. ► The LPU-algorithm is the most appropriate one for our data. ► We develop a four stage classification process. ► LPU classifies 90% of the documents accurately into positive, negative or unlabelled.
E-info
https://repository.uantwerpen.be/docman/iruaauth/c616b0/ecef703ab07.pdf
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