Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained
  Sentiment Analysis
Dan Ma Jun Xu Zongyu Wang Xuezhi Cao Yunsen Xian
Product reviews often contain a large number of implicit aspects and
object-attribute co-existence cases. Unfortunately, many existing studies in
Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can
make it difficult to extract opinions comprehensively and fairly. In this
paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple
Extraction (EASQE), which aims to hierarchically decompose aspect terms into
entities and aspects to avoid information loss, non-exclusive annotations, and
opinion misunderstandings in ABSA tasks. To facilitate research in this new
task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE,
and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have
also proposed a novel two-stage sequence-tagging based Trigger-Opinion
framework as the baseline for the EASQE task. Empirical evaluations show that
our Trigger-Opinion framework can generate satisfactory EASQE results and can
also be applied to other ABSA tasks, significantly outperforming
state-of-the-art methods. We have made the four datasets and source code of
Trigger-Opinion publicly available to facilitate further research in this area.