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（SA），旨在识别给定文本中的方面及其相关观点。在数字时代，ABSA在挖掘观点性文本数据以获取洞察并支持决策方面日益受到关注并广泛应用。ABSA研究采用语言学、统计学和机器学习方法，并利用标注数据集、方面情感词典及本体等资源。本质上，ABSA具有领域依赖性，且易受资源与应用程序领域之间错配的影响。然而据我们所知，现有ABSA文献综述尚未深入探讨这一问题。本文对ABSA研究进行系统文献综述（SLR），重点关注研究应用领域、数据集领域及研究方法，以考察其相互关系并识别时间趋势。结果表明，ABSA研究文献中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”数据集领域占据主导地位，且伴随监督机器学习等数据依赖型方法的广泛应用。本综述对ABSA研究领域做出以下独特贡献：1）据我们所知，这是首个通过系统视角将研究领域、数据集领域及研究方法关联起来的SLR；2）是ABSA领域规模最大的SLR之一，从4191篇搜索结果中筛选出519篇合格研究（无时间限制）；3）创新性地采用基于PDF挖掘的自动筛选流程，提升了筛选质量与可靠性。此外，本文亦讨论了研究建议及本综述的局限性。