  Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of
fine-grained sentiment tuples from sentences, aiming to discern the author's
opinions. Conventional methodologies predominantly rely on supervised
approaches; however, the efficacy of such methods diminishes in low-resource
domains lacking labeled datasets since they often lack the ability to
generalize across domains. To address this challenge, we propose a simple and
novel unsupervised approach to extract opinion terms and the corresponding
sentiment polarity for aspect terms in a sentence. Our experimental
evaluations, conducted on four benchmark datasets, demonstrate compelling
performance to extract the aspect oriented opinion words as well as assigning
sentiment polarity. Additionally, unsupervised approaches for opinion word
mining have not been explored and our work establishes a benchmark for the
same.
