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.

中文翻译：

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使用奇异频谱变换进行时间序列主题分析以检测政治经济周期

在这里，我们提出了一种新颖的主题变化检测方法，该方法将主题提取方法和变化点检测方法相结合。它从时间序列文本数据中提取主题作为每次的特征，并从提取的主题的变化模式中检测变化点。我们使用此方法分析了有价值的，尽管未被充分利用的文本数据集，其中包含日本首相（PM）长达32年的详细日常活动。所提出的方法和数据为政治经济周期的实证分析提供了新颖的见解，这是经济学和政治学中的经典问题。例如，由于我们的方法使我们能够直接观察和分析PM的行为，它可以克服由于PM行为不可观察而导致的以往研究遇到的经验挑战。我们的经验观察结果与该主题的最新理论发展基本一致。尽管存在局限性，但通过采用全新的方法和数据集，我们的方法增强了我们的理解并提供了对该经典问题的新见解。