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.

本文综述了情感分析在教育领域的四个级别及其在教学决策制定以及对教学质量的提升的作用，描述了使用 AI 方法和情感标注技术对学生反馈进行分析的过程和工具，并讨论了情感分析领域中的挑战和未来趋势。

教育数据的情感分析和舆情挖掘：一项调查

Sentiment analysis and opinion mining on educational data: A survey

Discovering new medicines is the hallmark of human endeavor to live a better
and longer life. Yet the pace of discovery has slowed down as we need to
venture into more wildly unexplored biomedical space to find one that matches
today's high standard. Modern AI-enabled by powerful computing, large
biomedical databases, and breakthroughs in deep learning-offers a new hope to
break this loop as AI is rapidly maturing, ready to make a huge impact in the
area. In this paper we review recent advances in AI methodologies that aim to
crack this challenge. We organize the vast and rapidly growing literature of AI
for drug discovery into three relatively stable sub-areas: (a) representation
learning over molecular sequences and geometric graphs; (b) data-driven
reasoning where we predict molecular properties and their binding, optimize
existing compounds, generate de novo molecules, and plan the synthesis of
target molecules; and (c) knowledge-based reasoning where we discuss the
construction and reasoning over biomedical knowledge graphs. We will also
identify open challenges and chart possible research directions for the years
to come.

此篇论文综述了近期在人工智能方法论中应用于药物发现的最新进展，并将其分为分子序列和几何图形的表征学习、基于数据驱动的推理以及基于知识的推理三个相对稳定的子领域，并探讨了未来可能的研究方向及挑战。