텍스트마이닝과 연관규칙을 이용한 외부감사 실시내용의 그룹별 핵심어 추출
Group-wise Keyword Extraction of the External Audit using Text Mining and Association Rules
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Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.
Abstract
Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.
참고문헌 (22)
- Aggarwal, C., "Mining Text Data", pp.163~122 (2012), Springer
- Alloghani, M., "Supervised and Unsupervised Learning for Data Science", pp.3~21 (2019), Springer
- 정건용, "재정정보 활용을 위한 텍스트 마이닝 기반 회계용어 형태소 분석기 구축", 정보시스템연구, 28(4), pp.155~174 (2019), 한국정보시스템학회[DOI]
- Blokdijk, H., "An Analysis of Cross-Sectional Differences in Big and Non-Big Public Accounting Firms’ Audit Programs", Auditing: A Journal of Practice & Theory, 25(1), pp.27~48 (2006)
- Boskou, G., "Assessing internal audit with text mining", Journal of Information & Knowledge Management, 17(02), pp.1850020~ (2018)
- Breiman, L, "Random Forests", Machine Learning, 45(1), pp.5~32 (2001)
- Byrnes, P. E., "Continuous auditing" (2018), Emerald Publishing Limited
- Chan, D. Y., "Continuous Auditing:Theory and Application", pp.271~283 (2018), Emerald Publishing Limited
- Cho, S., "Learning from Machine Learning in Accounting and Assurance", Journal of Emerging Technologies in Accounting, 17(1), pp.1~10 (2020)
- Goltz, N., "Enhancing regulatory compliance by using artificial intelligence text mining to identify penalty clauses in legislation", RAIL, 1, pp.175~ (2018)
- Gray, G. L., "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits", International Journal of Accounting Information Systems, 15(4), pp.357~380 (2014)
- Hastie T, "The Elements of Statistical Learning; Data Mining, Inference, and Prediction" (2001), Spring
- 허봉구, "데이터마이닝 앙상블기법의 감사위험 저감효과", 경영학연구, 42(5), pp.1523~1559 (2013), 한국경영학회
- Jung, G. Y., "Development of Text Mining-Based Accounting Terminology", ~ (2019)
- Kamaruddin, S. S., "A text mining system for deviation detection in financial documents", Intelligent Data Analysis, 19(s1), pp.S19~S44 (2015)
- 김동영, "경제정의지수가 우수한 기업의 내부회계관리제도 담당자의 특성이 감사시간에 미치는 영향", 대한경영학회지, 32(6), pp.1087~1107 (2019), 대한경영학회[DOI]
- 김경수, "특허 마이닝을 이용한 국방과학기술 연결망 연구", 품질경영학회지, 49(1), pp.97~112 (2021), 한국품질경영학회[DOI]
- 김상웅, "탐색적 자료 분석 및 연관규칙 분석을 활용한 잔류농약 부적합 농업인 유형 분석", 품질경영학회지, 49(1), pp.81~95 (2021), 한국품질경영학회[DOI]
- 권수영, "감사계획과 중간감사가 감사시간과 감사보수에 미치는 영향", 회계ㆍ세무와 감사 연구, 58(3), pp.137~172 (2016), 한국공인회계사회[DOI]
- 나형종, "감사보고서의 비정형 내용분석과 감사보수 및 시간을 이용한 감사의견의 적정성 연구 : 텍스트 마이닝과 감성분석 기법 적용을 중심으로", 회계학연구, 44(4), pp.175~214 (2019), 한국회계학회[DOI]
- Siroky, D. S, "Navigating and Random Forest and Related Advances in Algorithmic Modeling", Statistics Survey, 3, pp.147~163 (2009)
- Strobl, C., "An Introduction to the Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging and Random Forests", Psychological Methods, 14(4), pp.323~333 (2009)