  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. To our knowledge, it is also the
first to systematically examine the interrelations among ABSA research and data
distribution across domains, as well as trends in solution paradigms and
approaches. Our sample includes 727 primary studies screened from 8550 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.
