A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning

TL;DR

This paper introduces a hybrid transfer learning approach for Aspect-Based Sentiment Analysis that combines large language models with syntactic dependency structures to improve aspect term extraction and sentiment classification, especially in domain-specific contexts.

Contribution

It proposes a novel hybrid method leveraging both LLMs and syntactic dependencies to generate weakly-supervised annotations for ABSA, addressing data annotation challenges.

Findings

Effective in extracting aspect terms across domains

Improves sentiment classification accuracy

Reduces reliance on manually annotated datasets

Abstract

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)