  Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.
