Opinion mining is often regarded as a classification or segmentation task, involving the prediction of i) subjective expressions, ii) their target and iii) their polarity. Intuitively, these three variables are bidirectionally interdependent, but most work has either attempted to predict them in isolation or proposing pipeline-based approaches that cannot model the bidirectional interaction between these variables. Towards better understanding the interaction between these variables, we propose a model that allows for analyzing the relation of target and subjective phrases in both directions, thus providing an upper bound for the impact of a joint model in comparison to a pipeline model. We report results on two public datasets (cameras and cars), showing that our model outperforms state-of-the-art models, as well as on a new dataset consisting of Twitter posts