The Journal for Information Professionals
Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.
ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)
Editor : Seung Hyun Kim
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Past Issue
| Date | December 2022 |
|---|---|
| Vol. No. | Vol. 32 No. 4 |
| DOI | https://doi.org/10.14329/apjis.2022.32.4.770 |
| Page | 770~802 |
| Title | Applications of the Text Mining Approach to Online Financial Information |
| Author | Hansol Lee, Juyoung Kang, Sangun Park |
| Keyword | Text Mining, Finance, Fiscal Policy, Financial Information, Keyword Analysis, Social Network Analysis, Sentiment Analysis, Topic Modeling, Machine Learning |
| Abstract | With the development of deep learning techniques, text mining is producing breakthrough performance improvements, promising future applications, and practical use cases across many fields. Likewise, even though several attempts have been made in the field of financial information, few cases apply the current technological trends. Recently, companies and government agencies have attempted to conduct research and apply text mining in the field of financial information. First, in this study, we investigate various works using text mining to show what studies have been conducted in the financial sector. Second, to broaden the view of financial application, we provide a description of several text mining techniques that can be used in the field of financial information and summarize various paradigms in which these technologies can be applied. Third, we also provide practical cases for applying the latest text mining techniques in the field of financial information to provide more tangible guidance for those who will use text mining techniques in finance. Lastly, we propose potential future research topics in the field of financial information and present the research methods and utilization plans. This study can motivate researchers studying financial issues to use text mining techniques to gain new insights and improve their work from the rich information hidden in text data. |
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