  Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword
expressions (MWEs) on which sentiments are expressed and the sentiment
polarities associated with them. The development of supervised models has been
at the forefront of research in this area. However, training these models
requires the availability of manually annotated datasets which is both
expensive and time-consuming. Furthermore, the available annotated datasets are
tailored to a specific domain, language, and text type. In this work, we
address this notable challenge in current state-of-the-art ABSA research. We
propose a hybrid approach for Aspect Based Sentiment Analysis using transfer
learning. The approach focuses on generating weakly-supervised annotations by
exploiting the strengths of both large language models (LLM) and traditional
syntactic dependencies. We utilise syntactic dependency structures of sentences
to complement the annotations generated by LLMs, as they may overlook
domain-specific aspect terms. Extensive experimentation on multiple datasets is
performed to demonstrate the efficacy of our hybrid method for the tasks of
aspect term extraction and aspect sentiment classification.
  Keywords: Aspect Based Sentiment Analysis, Syntactic Parsing, large language
model (LLM)
