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)
Abstract (translated)
Aspect-Based Sentiment Analysis（ABSA）旨在识别表达情感的术语或多词表达（MWE），以及它们所关联的情感极性。在这个领域，有监督模型的开发始终处于研究的最前沿。然而，为了训练这些模型，需要提供手动标注的数据，这既耗资又耗时。此外，已有的标注数据都是针对特定领域、语言和文本类型的。在这篇论文中，我们着手解决当前状态下的ABSA研究中的一个重要挑战。我们提出了一种使用迁移学习进行 aspects-based sentiment analysis 的混合方法。该方法利用大型语言模型的优势，同时利用传统语法的句法结构来补充LLM生成的标注。我们利用LLM的句法结构来补充生成的标注，因为它们可能忽视了领域特定的 aspect terms。在多个数据集上进行大量实验，以证明我们的混合方法在 aspect term 提取和 aspect sentiment classification 等方面的有效性。关键词：Aspect-Based Sentiment Analysis，Syntactic Parsing，large language model (LLM)
URL
https://arxiv.org/abs/2403.17254
