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Sentiments in opinionated text are often determined by both aspects andtarget words (or targets). We observe that targets and aspects interrelate insubtle ways, often yielding conflicting sentiments. Thus, a naive aggregationof sentiments from aspects and targets treated separately, as in existingsentiment analysis models, impairs performance.  We propose Octa, an approach that jointly considers aspects and targets wheninferring sentiments. To capture and quantify relationships between targets andcontext words, Octa uses a selective self-attention mechanism that handlesimplicit or missing targets. Specifically, Octa involves two layers ofattention mechanisms for, respectively, selective attention between targets andcontext words and attention over words based on aspects. On benchmark datasets,Octa outperforms leading models by a large margin, yielding (absolute) gains inaccuracy of 1.6% to 4.3%.

Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis