Inter-party communication is crucial in representative democracies, facilitating information exchange and dialogue among political parties. Despite its importance, research on this topic remains limited due to lacking conceptual clarity and challenges in large-scale measurement. This article offers a comprehensive definition of inter-party communication as public communication by parties about others, with a positive, neutral, or negative stance, focusing on collaboration, policy, or personal issues. To effectively measure this phenomenon, we introduce a novel transformer-based approach capable of automatically classifying large volumes of text. Case studies on coalition signals in Germany and negative campaigning in Austria demonstrate its effectiveness. The study deepens our understanding of party competition, advances methods of automated text classification, and enables new research on political communication.
