  Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or
text spans, with the end goal of performing aspect-based sentiment analysis.
The small amount of available datasets for supervised ATE and the fact that
they cover only a few domains raise the need for exploiting other data sources
in new and creative ways. Publicly available review corpora contain a plethora
of opinionated aspect terms and cover a larger domain spectrum. In this paper,
we first propose a method for using such review corpora for creating a new
dataset for ATE. Our method relies on an attention mechanism to select
sentences that have a high likelihood of containing actual opinionated aspects.
We thus improve the quality of the extracted aspects. We then use the
constructed dataset to train a model and perform ATE with distant supervision.
By evaluating on human annotated datasets, we prove that our method achieves a
significantly improved performance over various unsupervised and supervised
baselines. Finally, we prove that sentence selection matters when it comes to
creating new datasets for ATE. Specifically, we show that, using a set of
selected sentences leads to higher ATE performance compared to using the whole
sentence set.
