Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model on existing datasets and compared it to other approaches.