Discovering Meaningful Trends in the Inaugural Addresses of United States Presidents Via Text Mining
Su Gon Cho, Jaehee Cho, Seoung Bum Kim · Journal of Korean Institute of Industrial Engineers · 2015
Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study, we propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and low-dimensional embedding. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of the presidents of the United States from 1789 to 2009. The main results of this study show that trends in the national policy agenda can be discovered based on clustering and visualization algorithms.