  Sentiments in opinionated text are often determined by both aspects and
target words (or targets). We observe that targets and aspects interrelate in
subtle ways, often yielding conflicting sentiments. Thus, a naive aggregation
of sentiments from aspects and targets treated separately, as in existing
sentiment analysis models, impairs performance.
  We propose Octa, an approach that jointly considers aspects and targets when
inferring sentiments. To capture and quantify relationships between targets and
context words, Octa uses a selective self-attention mechanism that handles
implicit or missing targets. Specifically, Octa involves two layers of
attention mechanisms for, respectively, selective attention between targets and
context words and attention over words based on aspects. On benchmark datasets,
Octa outperforms leading models by a large margin, yielding (absolute) gains in
accuracy of 1.6% to 4.3%.
