  Aspect-Based Sentiment Analysis (ABSA) is increasingly crucial in Natural
Language Processing (NLP) for applications such as customer feedback analysis
and product recommendation systems. ABSA goes beyond traditional sentiment
analysis by extracting sentiments related to specific aspects mentioned in the
text; existing attention-based models often need help to effectively connect
aspects with context due to language complexity and multiple sentiment
polarities in a single sentence. Recent research underscores the value of
integrating syntactic information, such as dependency trees, to understand
long-range syntactic relationships better and link aspects with context.
Despite these advantages, challenges persist, including sensitivity to parsing
errors and increased computational complexity when combining syntactic and
semantic information. To address these issues, we propose Amplifying
Aspect-Sentence Awareness (A3SN), a novel technique designed to enhance ABSA
through amplifying aspect-sentence awareness attention. Following the
transformer's standard process, our innovative approach incorporates multi-head
attention mechanisms to augment the model with sentence and aspect semantic
information. We added another multi-head attention module: amplify
aspect-sentence awareness attention. By doubling its focus between the sentence
and aspect, we effectively highlighted aspect importance within the sentence
context. This enables accurate capture of subtle relationships and
dependencies. Additionally, gated fusion integrates feature representations
from multi-head and amplified aspect-sentence awareness attention mechanisms,
which is essential for ABSA. Experimental results across three benchmark
datasets demonstrate A3SN's effectiveness and outperform state-of-the-art
(SOTA) baseline models.
