论文标题
八八：目标情绪分析中的遗漏和冲突
Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis
论文作者
论文摘要
有见识的文本中的情感通常由方面和目标词（或目标）决定。我们观察到目标和方面以微妙的方式相互关联，通常会产生冲突的情感。因此，与现有情感分析模型一样，来自各个方面和目标的情感幼稚聚集会损害绩效。 我们提出了八章，这种方法在推断情感时共同考虑方面和目标。为了捕获和量化目标和上下文单词之间的关系，Octa使用了处理隐式或丢失目标的选择性自我发挥机制。具体而言，OCTA分别涉及两个层次的注意机制，分别是基于方面的目标和上下文单词之间的选择性关注。在基准数据集上，八颗八块的优于领先模型的幅度较大，在1.6％至4.3％的准确性上获得（绝对）提高。
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%.
