This study integrates network and content analyses to examine valence-based homophily on Twitter or the tendency for individuals to interact with those expressing similar valence. During the 2012 federal election cycle, we collected Twitter conversations about 10 controversial political topics and mapped their network ties. Using network analysis, we discovered clusters—subgroups of highly self-connected users—and coded messages in each cluster for their expressed positive-to-negative emotional valence, level of support or opposition, and political leaning. We found that valence-based homophily successfully explained the selection of user interactions on Twitter, in terms of expressed emotional valence in their tweets or support versus criticism to an issue. It also finds conservative voices to be associated with negatively valenced clusters and vice versa. This study expands the theory of homophily beyond its traditional conceptualization and provides a new understanding of political-issue interactions in a social media context.