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 SNAT , an approach that jointly considers aspects and targets when inferring sentiments. To capture and quantify relationships between targets and context words, SNAT uses a selective self-attention mechanism that handles implicit or missing targets. Specifically, SNAT involves two layers of attention mechanisms  , respectively, for selective attention between targets and context words and attention over words based on aspects. On benchmark datasets, SNAT outperforms leading models by a large margin, yielding (absolute) gains in accuracy of 1.8\% to 5.2 \%.
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 \%.
R
SNAT
Octa
R
SNAT
Octa
R
SNAT
Octa
A
for
D
for
R
SNAT
Octa
R
1.8\% to 5.2
1.6\% to 4.3