Aspect-based Sentiment Analysis (ABSA) is a fine-grained type of sentiment analysis that identifies aspects and their associated opinions from a given text. With the surge of digital opinionated text data, ABSA gained increasing popularity for its ability to mine more detailed and targeted insights. Many review papers on ABSA subtasks and solution methodologies exist, however, few focus on trends over time or systemic issues relating to research application domains, datasets, and solution approaches. To fill the gap, this paper presents a Systematic Literature Review (SLR) of ABSA studies with a focus on trends and high-level relationships among these fundamental components. This review is one of the largest SLRs on ABSA, and also, to our knowledge, the first that systematically examines the trends and inter-relations among ABSA research and data distribution across domains and solution paradigms and approaches. Our sample includes 519 primary studies screened from 4191 search results without time constraints via an innovative automatic filtering process. Our quantitative analysis not only identifies trends in nearly two decades of ABSA research development but also unveils a systemic lack of dataset and domain diversity as well as domain mismatch that may hinder the development of future ABSA research. We discuss these findings and their implications and propose suggestions for future research.

翻译：面向方面的情感分析（ABSA）是一种细粒度的情感分析技术，旨在从给定文本中识别方面及其相关观点。随着数字化的意见文本数据激增，ABSA因其能够挖掘更详细、更具针对性的见解而日益受到关注。尽管存在大量关于ABSA子任务和解决方案方法的综述论文，但很少有研究关注时间趋势或与研究方向领域、数据集和解决方案方法相关的系统性问题。为填补这一空白，本文对ABSA研究进行了系统文献综述（SLR），重点关注这些基本组成部分的趋势和高层关系。本综述是规模最大的ABSA系统文献综述之一，并且据我们所知，也是首个系统性地分析ABSA研究趋势与跨领域数据分布、解决范式及方法之间相互关系的综述。我们的样本涵盖通过创新性自动筛选流程从4191个搜索结果中筛选出的519篇主要研究，未设时间限制。定量分析不仅揭示了近二十年ABSA研究发展的趋势，还发现数据集与领域多样性的系统性缺乏以及领域不匹配问题，这些问题可能阻碍未来ABSA研究的发展。我们讨论了这些发现及其启示，并为未来研究提出了建议。