This research employs automated text analysis to explore how textual characteristics in campaign emails affect monetary donations received by political candidates. The authors outline a new methodological framework that combines a machine learning approach for natural language processing with fixed effect regressions, thereby enabling researchers to study and interpret the impact of textual characteristics on donations while also accounting for individual differences across candidates and their email recipients. Using this framework, the authors analyze 764 emails from 19 candidates in the 2020 U.S. Democratic presidential primary election and evaluate how certain textual characteristics (e.g., empathy, vulnerability) in campaign emails affect donation outcomes. Identifying these effects would enable candidates to improve their email text and increase their donations by 9% on average. This research provides a practical and flexible roadmap for automated text analysis in situations where political campaigns do not have clear a priori hypotheses about which textual characteristics will be effective for them.
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