文章基本信息

标题：Textual analysis and machine leaning: Crack unstructured data in finance and accounting
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作者：Li Guo ; Li Guo ; Feng Shi 等
期刊名称：The Journal of Finance and Data Science
印刷版ISSN：2405-9188
出版年度：2016
卷号：2
期号：3
页码：153-170
DOI：10.1016/j.jfds.2017.02.001
语种：English
出版社：Elsevier
摘要：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.
关键词：Machine learning;Textual analysis;Finance;Accounting;Media news;Sentiment;Information