Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion

Abstract Citizens’ opinions about politicians are shaped by their perceptions of politicians’ personalities, characters, and traits. While prior research has investigated the traits voters value in politicians, less attention has been given to the traits politicians project in their public communication. This may stem from challenges in defining politicians’ public personality traits and measuring them at scale using computational text analysis. To address this challenge, we propose a computational approach that builds on public statements ( personality cues ) to infer politicians’ personalities from textual data. To do so, we operationalize two key political traits—agency and communion—using a theory-driven, domain-specific framework. We then compare various computational text analysis methods for extracting these traits from a large corpus of politicians’ parliamentary speeches, social media posts, and interviews. We validate our approach using a comprehensive set of human-labeled data, functional tests, and analyses of how prominently personality traits appear in the statements of German politicians and in the 2024 U.S. presidential debate between Donald Trump and Kamala Harris. Our findings indicate that prompting based techniques, particularly those leveraging advanced models such as DeepSeek-V3, outperform supervised and semisupervised methods. These results point to promising directions for advancing political psychology.