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Automatic classification of accounting literature

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  • Chakraborty, Vasundhara
  • Chiu, Victoria
  • Vasarhelyi, Miklos

Abstract

This paper explores the possibility of using semantic parsing, information retrieval and data mining techniques to automatically classify accounting research. Literature taxonomization plays a critical role in understanding a discipline's knowledge attributes and structure. The traditional research classification is a manual process which is considerably time consuming and may introduce inconsistent classifications by different experts. Aiming at aiding this classification issue, this study conducted three studies to seek the most effective and accurate method to classify accounting publications' attributes. We found results in the third study most rewarding in which the classification accuracy reached 87.27% with decision trees and rule-based algorithms applied. Findings in the first and second studies also provided valuable implications on automatic literature classifications, e.g. abstracts are better measures to use than keywords and balancing under-represented subclasses does not contribute to more accurate classifications. All three studies' results also suggest that expanding article sample size is a key to strengthen automatic classification accuracy. Overall, the potential path of this line of research seems to be very promising and would have several collateral benefits and applications.

Suggested Citation

  • Chakraborty, Vasundhara & Chiu, Victoria & Vasarhelyi, Miklos, 2014. "Automatic classification of accounting literature," International Journal of Accounting Information Systems, Elsevier, vol. 15(2), pages 122-148.
    • Handle: RePEc:eee:ijoais:v:15:y:2014:i:2:p:122-148
    DOI: 10.1016/j.accinf.2014.01.001
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    References listed on IDEAS

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    Cited by:

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    4. José M. Merigó & Jian-Bo Yang, 2017. "Accounting Research: A Bibliometric Analysis," Australian Accounting Review, CPA Australia, vol. 27(1), pages 71-100, March.
    5. Ferhat D. Zengul & Nurettin Oner & James D. Byrd & Arline Savage, 2021. "Revealing Research Themes and Trends in 30 Top‐ranking Accounting Journals: A Text‐mining Approach," Abacus, Accounting Foundation, University of Sydney, vol. 57(3), pages 468-501, September.
    6. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    7. Hutchison, Paul D. & Daigle, Ronald J. & George, Benjamin, 2018. "Application of latent semantic analysis in AIS academic research," International Journal of Accounting Information Systems, Elsevier, vol. 31(C), pages 83-96.
    8. Ferhat D. Zengul & James D. Byrd & Nurettin Oner & Mark Edmonds & Arline Savage, 2019. "Exploring corporate governance research in accounting journals through latent semantic and topic analyses," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 175-192, October.
    9. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).

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