Detecting Transition of Research Themes using Time-oriented Attributes in Governmental Funding
Abstract
We investigate a method for detecting yearly difference between new and old scientific research themes in grant applications. While open data for such analysis is available, there has not yet been sufficient study to fill in the gap between theory and practice of quantitative analysis of actual data. In our approach, binary document classification and regression analysis are combined to examine a large corpus of grant applications. From a theoretical viewpoint, we analyzed artificial corpora that emulates heterogeneity in the target text data. Then, we experimented on the real data of research themes in governmental funding in Japan to confirm the effectiveness of our approach. Our contribution in this study is represented by the notable findings as follows. (1) As research themes in competitive grants somewhat changed each year, newer themes gradually became dissimilar to old themes. (2) While the differences in a shorter span is generally smaller and different research areas have different tendencies in a longer span, the time-oriented tendency in research themes for 20 years were detectable and the differences between the baseline and our methods were statistically significant.
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