  In recent years, several online platforms have seen a rapid increase in the
number of review systems that request users to provide aspect-level feedback.
Document-level Multi-aspect Sentiment Classification (DMSC), where the goal is
to predict the ratings/sentiment from a review at an individual aspect level,
has become a challenging and imminent problem. To tackle this challenge, we
propose a deliberate self-attention-based deep neural network model, namely
FEDAR, for the DMSC problem, which can achieve competitive performance while
also being able to interpret the predictions made. FEDAR is equipped with a
highway word embedding layer to transfer knowledge from pre-trained word
embeddings, an RNN encoder layer with output features enriched by pooling and
factorization techniques, and a deliberate self-attention layer. In addition,
we also propose an Attention-driven Keywords Ranking (AKR) method, which can
automatically discover aspect keywords and aspect-level opinion keywords from
the review corpus based on the attention weights. These keywords are
significant for rating predictions by FEDAR. Since crowdsourcing annotation can
be an alternate way to recover missing ratings of reviews, we propose a
LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context
of multi-task learning, so that valuable human resources can focus on the most
uncertain predictions. Our extensive set of experiments on five different
open-domain DMSC datasets demonstrate the superiority of the proposed FEDAR and
LEAD models. We further introduce two new DMSC datasets in the healthcare
domain and benchmark different baseline models and our models on them.
Attention weights visualization results and visualization of aspect and opinion
keywords demonstrate the interpretability of our model and the effectiveness of
our AKR method.
