  Sentiment analysis AKA opinion mining is one of the most widely used NLP
applications to identify human intentions from their reviews. In the education
sector, opinion mining is used to listen to student opinions and enhance their
learning-teaching practices pedagogically. With advancements in sentiment
annotation techniques and AI methodologies, student comments can be labelled
with their sentiment orientation without much human intervention. In this
review article, (1) we consider the role of emotional analysis in education
from four levels: document level, sentence level, entity level, and aspect
level, (2) sentiment annotation techniques including lexicon-based and
corpus-based approaches for unsupervised annotations are explored, (3) the role
of AI in sentiment analysis with methodologies like machine learning, deep
learning, and transformers are discussed, (4) the impact of sentiment analysis
on educational procedures to enhance pedagogy, decision-making, and evaluation
are presented. Educational institutions have been widely invested to build
sentiment analysis tools and process their student feedback to draw their
opinions and insights. Applications built on sentiment analysis of student
feedback are reviewed in this study. Challenges in sentiment analysis like
multi-polarity, polysemous, negation words, and opinion spam detection are
explored and their trends in the research space are discussed. The future
directions of sentiment analysis in education are discussed.
