Aspect-based Sentiment Analysis (ABSA) is a type of fine-grained sentiment analysis (SA) that identifies aspects and the associated opinions from a given text. In the digital era, ABSA gained increasing popularity and applications in mining opinionated text data to obtain insights and support decisions. ABSA research employs linguistic, statistical, and machine-learning approaches and utilises resources such as labelled datasets, aspect and sentiment lexicons and ontology. By its nature, ABSA is domain-dependent and can be sensitive to the impact of misalignment between the resource and application domains. However, to our knowledge, this topic has not been explored by the existing ABSA literature reviews. In this paper, we present a Systematic Literature Review (SLR) of ABSA studies with a focus on the research application domain, dataset domain, and the research methods to examine their relationships and identify trends over time. Our results suggest a number of potential systemic issues in the ABSA research literature, including the predominance of the ``product/service review'' dataset domain among the majority of studies that did not have a specific research application domain, coupled with the prevalence of dataset-reliant methods such as supervised machine learning. This review makes a number of unique contributions to the ABSA research field: 1) To our knowledge, it is the first SLR that links the research domain, dataset domain, and research method through a systematic perspective; 2) it is one of the largest scoped SLR on ABSA, with 519 eligible studies filtered from 4191 search results without time constraint; and 3) our review methodology adopted an innovative automatic filtering process based on PDF-mining, which enhanced screening quality and reliability. Suggestions and our review limitations are also discussed.

翻译：基于方面的情感分析（ABSA）是一种细粒度情感分析方法，旨在从给定文本中识别方面及其关联观点。在数字时代，ABSA 在挖掘观点文本数据以获取洞察并支持决策方面日益受到关注并得到广泛应用。ABSA 研究采用语言学、统计学及机器学习方法，并利用标注数据集、方面与情感词典及本体等资源。本质上，ABSA 具有领域依赖性，且容易受到资源与应用领域间错配影响。然而，据我们所知，现有 ABSA 文献综述尚未探究该话题。本文对 ABSA 研究进行了系统文献综述，重点关注研究应用领域、数据集领域及研究方法，以考察其关联性并识别时间趋势。结果表明，ABSA 研究文献存在若干潜在系统性问题，包括大多数未明确研究应用领域的研究中“产品/服务评论”数据集领域占主导地位，同时依赖数据集的方法（如监督式机器学习）普遍存在。本综述对 ABSA 研究领域做出了以下独特贡献：1) 据我们所知，这是首篇通过系统视角链接研究领域、数据集领域及研究方法的系统文献综述；2) 这是涵盖范围最广的 ABSA 系统文献综述之一，在无时间限制下从 4191 条搜索结果中筛选出 519 项合格研究；3) 本综述采用基于 PDF 挖掘的创新性自动筛选流程，提升了筛选质量与可靠性。此外，本文还讨论了相关建议及本综述的局限性。