Publication Type
Journal Article
Version
publishedVersion
Publication Date
9-2016
Abstract
In finance and accounting, relative to quantitative methods traditionally used, textual analysis becomes popular recently despite of its substantially less precise manner. In an overview of the literature, we describe various methods used in textual analysis, especially machine learning. By comparing their classification performance, we find that neural network outperforms many other machine learning techniques in classifying news category. Moreover, we highlight that there are many challenges left for future development of textual analysis, such as identifying multiple objects within one single document.
Keywords
Machine learning, Textual analysis, Finance, Accounting, Media news, Sentiment, Information
Discipline
Finance | Finance and Financial Management
Research Areas
Finance
Publication
Journal of Finance and Data Science
Volume
2
Issue
3
First Page
153
Last Page
170
ISSN
2405-9188
Identifier
10.1016/j.jfds.2017.02.001
Publisher
Elsevier
Citation
GUO, Li; SHI, Feng; and TU, Jun.
Textual analysis and machine leaning: Crack unstructured data in finance and accounting. (2016). Journal of Finance and Data Science. 2, (3), 153-170.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5407
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jfds.2017.02.001