  Aspect-based Sentiment Analysis (ABSA) evaluates sentiment expressions within
a text to comprehend sentiment information. Previous studies integrated
external knowledge, such as knowledge graphs, to enhance the semantic features
in ABSA models. Recent research has examined the use of Graph Neural Networks
(GNNs) on dependency and constituent trees for syntactic analysis. With the
ongoing development of ABSA, more innovative linguistic and structural features
are being incorporated (e.g. latent graph), but this also introduces complexity
and confusion. As of now, a scalable framework for integrating diverse
linguistic and structural features into ABSA does not exist. This paper
presents the Extensible Multi-Granularity Fusion (EMGF) network, which
integrates information from dependency and constituent syntactic, attention
semantic , and external knowledge graphs. EMGF, equipped with multi-anchor
triplet learning and orthogonal projection, efficiently harnesses the combined
potential of each granularity feature and their synergistic interactions,
resulting in a cumulative effect without additional computational expenses.
Experimental findings on SemEval 2014 and Twitter datasets confirm EMGF's
superiority over existing ABSA methods.
