  Analysis of a large amount of data has always brought value to institutions
and organizations. Lately, people's opinions expressed through text have become
a very important aspect of this analysis. In response to this challenge, a
natural language processing technique known as Aspect-Based Sentiment Analysis
(ABSA) has emerged. Having the ability to extract the polarity for each aspect
of opinions separately, ABSA has found itself useful in a wide range of
domains. Education is one of the domains in which ABSA can be successfully
utilized. Being able to understand and find out what students like and don't
like most about a course, professor, or teaching methodology can be of great
importance for the respective institutions. While this task represents a unique
NLP challenge, many studies have proposed different approaches to tackle the
problem. In this work, we present a comprehensive review of the existing work
in ABSA with a focus in the education domain. A wide range of methodologies are
discussed and conclusions are drawn.
