Political discourses provide a forum for representatives to express their opinions and contribute towards policy making. Analyzing these discussions is crucial for recognizing possible delegates and making better voting choices in an independent nation. A politician's vote on a proposition is usually associated with their past discourses and impacted by cohesion forces in political parties. We focus on predicting a speaker's vote on a bill by augmenting linguistic models with temporal and cohesion contexts. We propose TEC, a time evolving graph based model that jointly employs links between motions, speakers, and temporal politician states. TEC outperforms competitive models, illustrating the benefit of temporal and contextual signals for predicting a politician's stance.