  Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the
sentiment polarity of aspect terms within sentences. Employing graph neural
networks to capture structural patterns from syntactic dependency parsing has
been confirmed as an effective approach for boosting ABSA. In most works, the
topology of dependency trees or dependency-based attention coefficients is
often loosely regarded as edges between aspects and opinions, which can result
in insufficient and ambiguous syntactic utilization. To address these problems,
we propose a new reinforced dependency graph convolutional network (RDGCN) that
improves the importance calculation of dependencies in both distance and type
views. Initially, we propose an importance calculation criterion for the
minimum distances over dependency trees. Under the criterion, we design a
distance-importance function that leverages reinforcement learning for weight
distribution search and dissimilarity control. Since dependency types often do
not have explicit syntax like tree distances, we use global attention and mask
mechanisms to design type-importance functions. Finally, we merge these weights
and implement feature aggregation and classification. Comprehensive experiments
on three popular datasets demonstrate the effectiveness of the criterion and
importance functions. RDGCN outperforms state-of-the-art GNN-based baselines in
all validations.
