Abstract
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