  Since the dawn of the digitalisation era, customer feedback and online
reviews are unequivocally major sources of insights for businesses.
Consequently, conducting comparative analyses of such sources has become the de
facto modus operandi of any business that wishes to give itself a competitive
edge over its peers and improve customer loyalty. Sentiment analysis is one
such method instrumental in gauging public interest, exposing market trends,
and analysing competitors. While traditional sentiment analysis focuses on
overall sentiment, as the needs advance with time, it has become important to
explore public opinions and sentiments on various specific subjects, products
and services mentioned in the reviews on a finer-granular level. To this end,
Aspect-based Sentiment Analysis (ABSA), supported by advances in Artificial
Intelligence (AI) techniques which have contributed to a paradigm shift from
simple word-level analysis to tone and context-aware analyses, focuses on
identifying specific aspects within the text and determining the sentiment
associated with each aspect. In this study, we compare several deep-NN methods
for ABSA on two benchmark datasets (Restaurant14 and Laptop-14) and found that
FAST LSA obtains the best overall results of 87.6% and 82.6% accuracy but does
not pass LSA+DeBERTa which reports 90.33% and 86.21% accuracy respectively.
