Research on Information Trend Analysis in the Field of Corporate Security: Application of Topic Modeling Technique
Jungtae Park, Keunwoo Kim, Yousub Hwang · The Journal of Internet Electronic Commerce Resarch · 2020
In rapidly changing economic environment, firms make numerous decisions to compete with other players and maximize profit in the turbulent market. Given the demands to process such decisions repeatedly, firms often seek an architecture that can handle large-scale unstructured data. Furthermore practitioners are seeking higher accessibility and computational efficiency by placing data on remote servers or clouds rather than local storages. Following these recent changes, firms recognize corporate security as a very important issue. The purpose of this study is to use text mining analysis techniques with the “security”-related keywords to identify the most recent trends in the real business world. For this purpose, we present a framework for Corporate Information Trend Analysis (CITA). News data were collected from BIGKinds database for 20 years, and topic modeling analysis was used on three different periods. The results clearly show how our framework can identify the changes in security-related topics and keywords over time. These attempts will also be applicable to future research of corporate information trends using other sets of keywords.