  For multiple aspects scenario of aspect-based sentiment analysis (ABSA),
existing approaches typically ignore inter-aspect relations or rely on temporal
dependencies to process aspect-aware representations of all aspects in a
sentence. Although multiple aspects of a sentence appear in a non-adjacent
sequential order, they are not in a strict temporal relationship as natural
language sequence, thus the aspect-aware sentence representations should not be
treated as temporal dependency processing. In this paper, we propose a novel
non-temporal mechanism to enhance the ABSA task through modeling inter-aspect
dependencies. Furthermore, we focus on the well-known class imbalance issue on
the ABSA task and address it by down-weighting the loss assigned to
well-classified instances. Experiments on two distinct domains of SemEval 2014
task 4 demonstrate the effectiveness of our proposed approach.
