  We present a simple but effective method for aspect identification in
sentiment analysis. Our unsupervised method only requires word embeddings and a
POS tagger, and is therefore straightforward to apply to new domains and
languages. We introduce Contrastive Attention (CAt), a novel single-head
attention mechanism based on an RBF kernel, which gives a considerable boost in
performance and makes the model interpretable. Previous work relied on
syntactic features and complex neural models. We show that given the simplicity
of current benchmark datasets for aspect extraction, such complex models are
not needed. The code to reproduce the experiments reported in this paper is
available at https://github.com/clips/cat
