  Aspect-based sentiment analysis (ABSA) is a natural language processing
problem that requires analyzing user-generated reviews to determine: a) The
target entity being reviewed, b) The high-level aspect to which it belongs, and
c) The sentiment expressed toward the targets and the aspects. Numerous yet
scattered corpora for ABSA make it difficult for researchers to identify
corpora best suited for a specific ABSA subtask quickly. This study aims to
present a database of corpora that can be used to train and assess autonomous
ABSA systems. Additionally, we provide an overview of the major corpora for
ABSA and its subtasks and highlight several features that researchers should
consider when selecting a corpus. Finally, we discuss the advantages and
disadvantages of current collection approaches and make recommendations for
future corpora creation. This survey examines 65 publicly available ABSA
datasets covering over 25 domains, including 45 English and 20 other languages
datasets.
