  With the constantly growing number of reviews and other sentiment-bearing
texts on the Web, the demand for automatic sentiment analysis algorithms
continues to expand. Aspect-based sentiment classification (ABSC) allows for
the automatic extraction of highly fine-grained sentiment information from text
documents or sentences. In this survey, the rapidly evolving state of the
research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the
ABSC models into three major categories: knowledge-based, machine learning, and
hybrid models. This taxonomy is accompanied with summarizing overviews of the
reported model performances, and both technical and intuitive explanations of
the various ABSC models. State-of-the-art ABSC models are discussed, such as
models based on the transformer model, and hybrid deep learning models that
incorporate knowledge bases. Additionally, various techniques for representing
the model inputs and evaluating the model outputs are reviewed. Furthermore,
trends in the research on ABSC are identified and a discussion is provided on
the ways in which the field of ABSC can be advanced in the future.
