Webersinke N (2023)
Publication Language: English
Publication Type: Other publication type
Publication year: 2023
DOI: 10.2139/ssrn.4527724
Deep learning approaches have revolutionized Natural Language Processing (NLP) and textual analysis in the last decade. The performance of the popular chatbot ChatGPT is an impressive demonstration of what deep learning is capable of. However, this remarkable progress with the potential to significantly reduce measurement error has apparently not yet reached accounting and finance. We review a large corpus of accounting and finance literature that uses textual analysis and observe that rule-based and traditional machine learning approaches still dominate. We then compare the performance of rule-based, traditional machine learning, and deep learning approaches on four datasets and find deep learning approaches to consistently perform best. However, we also find that for simpler topics requiring less context, rule-based approaches can produce decent results. These findings argue for more evaluation and increased, but not universal, use of deep learning for textual analysis in accounting and finance.
APA:
Webersinke, N. (2023). Natural Language Processing meets Accounting and Finance: Review and Performance Comparison of Textual Analysis Approaches.
MLA:
Webersinke, Nicolas. Natural Language Processing meets Accounting and Finance: Review and Performance Comparison of Textual Analysis Approaches. 2023.
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