A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning
This work addresses the data scarcity problem in ABSA for researchers and practitioners by reducing annotation costs, though it is incremental as it builds on existing transfer learning and syntactic methods.
The paper tackles the challenge of expensive manual annotation in Aspect-Based Sentiment Analysis by proposing a hybrid method using transfer learning with large language models and syntactic dependencies to generate weakly-supervised annotations, achieving competitive performance on multiple datasets for aspect term extraction and sentiment classification.
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)