Araştırma Makalesi
Yıl 2022,
Cilt: 15 Sayı: 3, 637 - 663, 01.11.2022
Öz
Metin madenciliği; bilgi bilimleri, dilbilim, bilgisayar bilimleri ve veri bilimleri gibi farklı alanlardan kavram ve teknikleri içeren çok disiplinli bir bilgi dalıdır. Kuruluşların kâğıt verilerden elektronik belgelere ve dijital kayıtlara geçmesiyle birlikte, iş süreçlerinin hızla dijitalleşmesi metin madenciliğine olan ilgiyi artırmıştır. Muhasebe alanındaki verilerin giderek büyümesinden dolayı metin madenciliği teknolojisi bu alan için önemli bir araştırma konusu olmuştur. Bu çalışmanın amacı; muhasebe alanında, metin madenciliğinin kullanımına yönelik bilgiler verilerek gelecekte bu teknolojinin kuruluşlara ve kişilere etkisini özlü bir şekilde ortaya koymaktır. Sonuç olarak metin madenciliği teknolojisinin muhasebe alanında kullanımı; muhasebe otomasyonu, denetim otomasyonu, vergi otomasyonu ve iş danışmanlığı otomasyonu şeklinde ele alınmış ve açıklamalar yapılmıştır. Ayrıca yapay zekâ ve makine öğrenmesi yaklaşımlarıyla birleştirilen metin madenciliğinin, işlemleri çok daha fazla otomatikleştirmesinden dolayı kuruluşlara ve muhasebe meslek mensuplarına önemli fırsatlar sunacağı ön görülmektedir.
Anahtar Kelimeler
Kaynakça
- Alarcon, J., Fine T. & Ng, C. (2019). Accounting AI and machine learning: Applications and challenges. Accounting and Technology: PICPA’s Guide to an Evolving Profession, 3-7. Çevrimiçi http://onlinedigeditions.com /publication/?m= 14667&i=583202&p=0.
- Aldhizer, G. R. (2017). Visual and text analytics. The CPA Journal, 87(6), 30-33. Çevrimiçi https://www.cpajournal.com/2017/06/20/visual-textanaly tics/.
- Blake, C. (2011). Text mining. Annual Review of Information Science and Technology, 45(1), 121-155. http://doi.org/10.1002/aris.2011.1440450110.
- Brown, B. & Rainey, S. (2018). Driving faster, more accurate and more beneficial tax decisions. IBM. Çevrimiçi https://www.ibm.com/blogs/watso n/2018/04/driving-faster-more-accurate-and-more-beneficial-tax-decisions/.
- Chopra, S., Auli, M. & Rush, A. M. (2016). Abstractive sentence summarization with attentive recurrent neural networks. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, California, United States. Çevrimiçi https://www. aclweb.org/anthology/N16-1012/.
- Da Costa Pereira, C. & Tettamanzi, A. G. B. (2006). An ontology-based method for user model acquisition. Studies in fuzziness and soft computing: Soft computing in ontologies and semantic web, Springer.
- Davis, M., Vashisth, S., Emmott, S. & Brethenoux, E. (2018). Market guide for text analytics (ID: G00361404). Retrieved From Gartner Database.
- Deloitte Harnesses The Power of Kira for Lease Accounting Contract Review. (2020). Kira Systems. Retrieved. Çevrimiçi https:// kirasystems.com/ resources/case-studies/deloitte/.
- Feldman, R. & Dagan, I. (1995). Knowledge discovery in textual databases. The First International Conference on Knowledge Discovery and Data Mining (KDD-95). Montreal, Quebec, Canada.
- Internal Revenue Service (2022). Advance data and analytics. Çevrimiçi https://www.irs.gov/about-irs/ strategic-goals/advance-data-analytics.
- H&R Block (2017). H&R Block with IBM Watson reinventing tax preparation. Çevrimiçi https://www.hrblock.com/tax-center/newsroom/ around-block/partnership-with-ibm-watson-reinventing-tax-prep/.
- Hearst, M. A. (2003). What is text mining? [Unpublished Essay]. Çevrimiçi http://people. ischool.berkeley.edu/~hearst/text-mining.html.
- Keikha, M., Razavian, N. S., Oroumchian, F. & Razi, H. S. (2008). Document representation and quality of text: An analysis. In Berry M. W. & Castellanos M. (Eds.), Survey of Text Mining II: Clustering, Classification, and Retrieval. Springer.
- Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1-8.
- KPMG and IBM (2020). KPMG. Retrieved. Çevrimiçi https://home. kpmg/xx/en/home/about/alliances/ibm.html.
- Kumar, B. S. & Ravi, V. (2016). A survey of the applications of text mining in the financial domain. Knowledge-Based Systems, 114, 128-147. https:// doi.org/10.1016/j.knosys.2016.10.003.
- Kumar, Y. J., Goh, O. S., Basiron, H., Choon, N. H. & Suppiah, P. C. (2016). A review on automatic text summarization approaches. Journal of Computer Science, 12(4), 178-190. https://doi.org/10.3844/jcssp.2016 .178.190.
- Lewis, C. & Young, S. (2019). Fad or future? Automated analysis of financial text and its implications for corporate reporting. Accounting and Business Research, 49(5), 587-615. https://doi.org/10.1080/00014788.201 9.1611730.
- Lowa State University. (2018). Textual analytics for accounting and auditing. Çevrimiçi https://www.ivybusiness.iastate.edu/files/2018/12/Janvrin-Textua l-Analysis-Presentation-Dec-14-2018.pdf.
- Nagarkar, S. & Kumbhar, R. (2015). Text mining: An analysis of research published under the subject category ‘information science library science’ in web of science database during 1999-2013. Library Review, 64(3), 248-262. https://doi-org.libproxy.temple.edu/10.1108/LR-08-2014-0091.
- Nallapati, R., Zhou, B., Dos Santos, C. N., Gulcehre, C. & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. The SIGNLL Conference on Computational Natural Language Learning (CoNLL). Çevrimiçi https://arxiv.org/abs/1602.06023.
- Pejic-Bach, M., Krstic Z., Seljan, S. & Turulja, L. (2019). Text mining for big data analysis in financial sector: A literature review. Sustainability, 11(5), 1277. http://doi.org/10.3390/su11051277.
- Rajman, M. & Vesely, M. (2004). From text to knowledge: Document processing and visualization: A Text Mining Approach. In S. Sirmakessis (Ed.), Text Mining and its Applications – Results of the NEMIS Launch Conference. Springer.
- Random House Kernerman Webster. (2020). Coreference. In Random House Kernerman Webster’s College Dictionary. Çevrimiçi https://www.thefree dictionary.com/coreference.
- Rush, A. M. Chopra, S. & Weston, J. (2015). A neural attention model for abstractive sentence summarization. Cornell University Library. Çevrimiçi https:// arxiv.org/abs/1509.00685.
- Sharda, R., Delen, D. & Turban, E. (2014). Business intelligence: A managerial perspective on analytics (3rd ed.). Pearson Prentice Hall.
- Song, S., Huang, H. & Ruan, T. (2019). Abstractive text summarization using LSTM-CNN based deep learning. Multimedia Tools and Applications, 78, 857-875. https://doi.org/10.1007/s11042-018-5749-3.
- Sparck-Jones, K. (1999). Automatic summarizing: Factors and directions. In I. Mani & M. T. Maybury (Eds.), Advances in automated text summarization, 1-12. MIT Press. Çevrimiçi https://www.cl.cam.ac.uk/archive/ksj21/ ksjdigipapers/summbook99.pdf.
- Sun, T. & Vasarhelyi, M. A. (2018). Embracing textual data analytics in auditing with deep learning. International Journal of Digital Accounting Research, 18, 49-67. https://doi.org/10.4192/1577-8517-v18_3.
- Tang, J., Hong, M., Zhang, D. L. & Li, J. (2008). Information extraction: Methodologies and applications. In H. do Prado & E. Ferneda (Eds.), Emerging technologies of text mining: Techniques and applications, 1-33. IGI Global.
- Torpey, D. & Walden, V. (2009). Accounting for words; Text analytics technology may help internal auditors uncover hidden risks and gain greater insight on business performance. Internal Auditor, 66(4), 40–44.
- Torres‐Moreno, J. M. (2014). Automatic Text Summarization. John Wiley & Sons, Inc.
- Tuffery, S. (2011). Text mining. In Wiley Series in Computational Statistics, Data Mining and Statistics for Decision Making, 627-636. John Wiley & Sons, Ltd.
- Verma, S. & Nidhi, V. (2019). Extractive summarization using deep learning. Cornell University Library. Çevrimiçi http://libproxy.temple.edu/login ?url=https://searchproquest.com.libproxy.temple.edu/docview/2075709212?accountid= 14270.
- Wang, G. (2019). Tech talk: Intuit’s AI-powered tax knowledge engine boosts filers’ confidence. Intuit Blog. Çevrimiçi https://www.intuit.com/blog/ social-responsibility/tech-talk-intuits-ai-powered-tax-knowledge-engine-boo sts-filers-confidence/.
- Zhang, Y., Er, M. J. & Pratama, M. (2016). Extractive document summarization based on Convolutional neural networks. IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. Florence, 918-922.
Yıl 2022,
Cilt: 15 Sayı: 3, 637 - 663, 01.11.2022
Öz
Text mining; It is a multidisciplinary branch of knowledge that includes concepts and techniques from different fields such as information sciences, linguistics, computer science and data science. With the transition of organizations from paper data to electronic documents and digital records, the rapid digitization of business processes has increased the interest in text mining. Due to the growing data in the field of accounting, text mining technology has become an important research topic for this field. The aim of this study; In the field of accounting, by giving information on the use of text mining, it is to reveal the effect of this technology on organizations and individuals in the future in a concise way. As a result, the use of text mining technology in the field of accounting; accounting automation, audit automation, tax automation and business consultancy automation. In addition, it is predicted that text mining combined with artificial intelligence and machine learning approaches will offer significant opportunities to organizations and accounting professionals, as it automates processes much more.
Anahtar Kelimeler
Kaynakça
- Alarcon, J., Fine T. & Ng, C. (2019). Accounting AI and machine learning: Applications and challenges. Accounting and Technology: PICPA’s Guide to an Evolving Profession, 3-7. Çevrimiçi http://onlinedigeditions.com /publication/?m= 14667&i=583202&p=0.
- Aldhizer, G. R. (2017). Visual and text analytics. The CPA Journal, 87(6), 30-33. Çevrimiçi https://www.cpajournal.com/2017/06/20/visual-textanaly tics/.
- Blake, C. (2011). Text mining. Annual Review of Information Science and Technology, 45(1), 121-155. http://doi.org/10.1002/aris.2011.1440450110.
- Brown, B. & Rainey, S. (2018). Driving faster, more accurate and more beneficial tax decisions. IBM. Çevrimiçi https://www.ibm.com/blogs/watso n/2018/04/driving-faster-more-accurate-and-more-beneficial-tax-decisions/.
- Chopra, S., Auli, M. & Rush, A. M. (2016). Abstractive sentence summarization with attentive recurrent neural networks. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, California, United States. Çevrimiçi https://www. aclweb.org/anthology/N16-1012/.
- Da Costa Pereira, C. & Tettamanzi, A. G. B. (2006). An ontology-based method for user model acquisition. Studies in fuzziness and soft computing: Soft computing in ontologies and semantic web, Springer.
- Davis, M., Vashisth, S., Emmott, S. & Brethenoux, E. (2018). Market guide for text analytics (ID: G00361404). Retrieved From Gartner Database.
- Deloitte Harnesses The Power of Kira for Lease Accounting Contract Review. (2020). Kira Systems. Retrieved. Çevrimiçi https:// kirasystems.com/ resources/case-studies/deloitte/.
- Feldman, R. & Dagan, I. (1995). Knowledge discovery in textual databases. The First International Conference on Knowledge Discovery and Data Mining (KDD-95). Montreal, Quebec, Canada.
- Internal Revenue Service (2022). Advance data and analytics. Çevrimiçi https://www.irs.gov/about-irs/ strategic-goals/advance-data-analytics.
- H&R Block (2017). H&R Block with IBM Watson reinventing tax preparation. Çevrimiçi https://www.hrblock.com/tax-center/newsroom/ around-block/partnership-with-ibm-watson-reinventing-tax-prep/.
- Hearst, M. A. (2003). What is text mining? [Unpublished Essay]. Çevrimiçi http://people. ischool.berkeley.edu/~hearst/text-mining.html.
- Keikha, M., Razavian, N. S., Oroumchian, F. & Razi, H. S. (2008). Document representation and quality of text: An analysis. In Berry M. W. & Castellanos M. (Eds.), Survey of Text Mining II: Clustering, Classification, and Retrieval. Springer.
- Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1-8.
- KPMG and IBM (2020). KPMG. Retrieved. Çevrimiçi https://home. kpmg/xx/en/home/about/alliances/ibm.html.
- Kumar, B. S. & Ravi, V. (2016). A survey of the applications of text mining in the financial domain. Knowledge-Based Systems, 114, 128-147. https:// doi.org/10.1016/j.knosys.2016.10.003.
- Kumar, Y. J., Goh, O. S., Basiron, H., Choon, N. H. & Suppiah, P. C. (2016). A review on automatic text summarization approaches. Journal of Computer Science, 12(4), 178-190. https://doi.org/10.3844/jcssp.2016 .178.190.
- Lewis, C. & Young, S. (2019). Fad or future? Automated analysis of financial text and its implications for corporate reporting. Accounting and Business Research, 49(5), 587-615. https://doi.org/10.1080/00014788.201 9.1611730.
- Lowa State University. (2018). Textual analytics for accounting and auditing. Çevrimiçi https://www.ivybusiness.iastate.edu/files/2018/12/Janvrin-Textua l-Analysis-Presentation-Dec-14-2018.pdf.
- Nagarkar, S. & Kumbhar, R. (2015). Text mining: An analysis of research published under the subject category ‘information science library science’ in web of science database during 1999-2013. Library Review, 64(3), 248-262. https://doi-org.libproxy.temple.edu/10.1108/LR-08-2014-0091.
- Nallapati, R., Zhou, B., Dos Santos, C. N., Gulcehre, C. & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. The SIGNLL Conference on Computational Natural Language Learning (CoNLL). Çevrimiçi https://arxiv.org/abs/1602.06023.
- Pejic-Bach, M., Krstic Z., Seljan, S. & Turulja, L. (2019). Text mining for big data analysis in financial sector: A literature review. Sustainability, 11(5), 1277. http://doi.org/10.3390/su11051277.
- Rajman, M. & Vesely, M. (2004). From text to knowledge: Document processing and visualization: A Text Mining Approach. In S. Sirmakessis (Ed.), Text Mining and its Applications – Results of the NEMIS Launch Conference. Springer.
- Random House Kernerman Webster. (2020). Coreference. In Random House Kernerman Webster’s College Dictionary. Çevrimiçi https://www.thefree dictionary.com/coreference.
- Rush, A. M. Chopra, S. & Weston, J. (2015). A neural attention model for abstractive sentence summarization. Cornell University Library. Çevrimiçi https:// arxiv.org/abs/1509.00685.
- Sharda, R., Delen, D. & Turban, E. (2014). Business intelligence: A managerial perspective on analytics (3rd ed.). Pearson Prentice Hall.
- Song, S., Huang, H. & Ruan, T. (2019). Abstractive text summarization using LSTM-CNN based deep learning. Multimedia Tools and Applications, 78, 857-875. https://doi.org/10.1007/s11042-018-5749-3.
- Sparck-Jones, K. (1999). Automatic summarizing: Factors and directions. In I. Mani & M. T. Maybury (Eds.), Advances in automated text summarization, 1-12. MIT Press. Çevrimiçi https://www.cl.cam.ac.uk/archive/ksj21/ ksjdigipapers/summbook99.pdf.
- Sun, T. & Vasarhelyi, M. A. (2018). Embracing textual data analytics in auditing with deep learning. International Journal of Digital Accounting Research, 18, 49-67. https://doi.org/10.4192/1577-8517-v18_3.
- Tang, J., Hong, M., Zhang, D. L. & Li, J. (2008). Information extraction: Methodologies and applications. In H. do Prado & E. Ferneda (Eds.), Emerging technologies of text mining: Techniques and applications, 1-33. IGI Global.
- Torpey, D. & Walden, V. (2009). Accounting for words; Text analytics technology may help internal auditors uncover hidden risks and gain greater insight on business performance. Internal Auditor, 66(4), 40–44.
- Torres‐Moreno, J. M. (2014). Automatic Text Summarization. John Wiley & Sons, Inc.
- Tuffery, S. (2011). Text mining. In Wiley Series in Computational Statistics, Data Mining and Statistics for Decision Making, 627-636. John Wiley & Sons, Ltd.
- Verma, S. & Nidhi, V. (2019). Extractive summarization using deep learning. Cornell University Library. Çevrimiçi http://libproxy.temple.edu/login ?url=https://searchproquest.com.libproxy.temple.edu/docview/2075709212?accountid= 14270.
- Wang, G. (2019). Tech talk: Intuit’s AI-powered tax knowledge engine boosts filers’ confidence. Intuit Blog. Çevrimiçi https://www.intuit.com/blog/ social-responsibility/tech-talk-intuits-ai-powered-tax-knowledge-engine-boo sts-filers-confidence/.
- Zhang, Y., Er, M. J. & Pratama, M. (2016). Extractive document summarization based on Convolutional neural networks. IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society. Florence, 918-922.
Toplam 36 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | İşletme |
| Bölüm | Sayı |
| Yazarlar | |
| Erken Görünüm Tarihi | 22 Eylül 2022 |
| Yayımlanma Tarihi | 1 Kasım 2022 |
| Gönderilme Tarihi | 16 Nisan 2022 |
| Kabul Tarihi | 21 Ağustos 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 15 Sayı: 3 |
Cited By
Siyaset biliminde otomatik metin analizi yöntemleri ve uygulama alanları
Hitit Sosyal Bilimler Dergisi
https://doi.org/10.17218/hititsbd.1260739
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