  Aspect sentiment coherency is an intriguing yet underexplored topic in the
field of aspect-based sentiment classification. This concept reflects the
common pattern where adjacent aspects often share similar sentiments. Despite
its prevalence, current studies have not fully recognized the potential of
modeling aspect sentiment coherency, including its implications in adversarial
defense. To model aspect sentiment coherency, we propose a novel local
sentiment aggregation (LSA) paradigm based on constructing a
differential-weighted sentiment aggregation window. We have rigorously
evaluated our model through experiments, and the results affirm the proficiency
of LSA in terms of aspect coherency prediction and aspect sentiment
classification. For instance, it outperforms existing models and achieves
state-of-the-art sentiment classification performance across five public
datasets. Furthermore, we demonstrate the promising ability of LSA in ABSC
adversarial defense, thanks to its sentiment coherency modeling. To encourage
further exploration and application of this concept, we have made our code
publicly accessible. This will provide researchers with a valuable tool to
delve into sentiment coherency modeling in future research.
