Time-series topic analysis using singular spectrum transformation for detecting political business cycles

Journal of Cloud Computing

Takafumi Nakanishi¹  Budrul Ahsan²  Sota Kato³  Hirokazu Shimauchi⁴ 

[1] Musashino University, Tokyo, Japan;Philips Japan, Tokyo, Japan;Tokyo Foundation for Policy Research, Tokyo, Japan/ International University of Japan, Tokyo, Japan;Tokyo Foundation for Policy Research, Tokyo, Japan;

关键词: Topic variation detection;    Political business cycles;    Early election;    Latent Dirichlet allocation;    Singular spectrum transformation;    Japanese politics;   

DOI  :  10.1186/s13677-020-00197-4

来源: Springer

【 摘 要 】

Herein, we present a novel topic variation detection method that combines a topic extraction method and a change-point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied this method to analyze the valuable, albeit underutilized, text dataset containing the Japanese Prime Minister’s (PM’s) detailed daily activities for over 32 years. The proposed method and data provide novel insights into the empirical analyses of political business cycles, which is a classical issue in economics and political science. For instance, as our approach enables us to directly observe and analyze the PM’s actions, it can overcome the empirical challenges encountered by previous research owing to the unobservability of the PM’s behavior. Our empirical observations are primarily consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely novel method and dataset, our approach enhances our understanding and provides new insights into this classic issue.

【 授权许可】

CC BY