  Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem
that entails the extraction of multifaceted aspects, opinions, and sentiments
from the given text. Both standalone and compound ABSA tasks have been
extensively used in the literature to examine the nuanced information present
in online reviews and social media posts. Current ABSA methods often rely on
static hyperparameters for attention-masking mechanisms, which can struggle
with context adaptation and may overlook the unique relevance of words in
varied situations. This leads to challenges in accurately analyzing complex
sentences containing multiple aspects with differing sentiments. In this work,
we present adaptive masking methods that remove irrelevant tokens based on
context to assist in Aspect Term Extraction and Aspect Sentiment Classification
subtasks of ABSA. We show with our experiments that the proposed methods
outperform the baseline methods in terms of accuracy and F1 scores on four
benchmark online review datasets. Further, we show that the proposed methods
can be extended with multiple adaptations and demonstrate a qualitative
analysis of the proposed approach using sample text for aspect term extraction.
