  Attention-based long short-term memory (LSTM) networks have proven to be
useful in aspect-level sentiment classification. However, due to the
difficulties in annotating aspect-level data, existing public datasets for this
task are all relatively small, which largely limits the effectiveness of those
neural models. In this paper, we explore two approaches that transfer knowledge
from document- level data, which is much less expensive to obtain, to improve
the performance of aspect-level sentiment classification. We demonstrate the
effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015,
and 2016, and we show that attention-based LSTM benefits from document-level
knowledge in multiple ways.
