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.

翻译：情感分析（又称意见挖掘）是自然语言处理中应用最广泛的技术之一，用于从用户评论中识别人类意图。在教育领域，意见挖掘被用于倾听学生意见，并从教学法角度优化学习-教学实践。随着情感标注技术与人工智能方法的进步，学生评论的情感倾向无需过多人工干预即可被自动标注。本综述从以下四个维度展开：（1）从文档级、句子级、实体级与方面级四个层面，探讨情感分析在教育中的作用；（2）梳理包括基于词典与基于语料库的无监督标注方法在内的情感标注技术；（3）讨论人工智能在情感分析中的作用，涵盖机器学习、深度学习及Transformer等方法；（4）阐述情感分析对优化教学法、决策制定与评估等教育流程的影响。教育机构已投入大量资源开发情感分析工具，以处理学生反馈并挖掘其观点与见解。本研究系统梳理了基于学生反馈情感分析的应用系统，并探讨了多极性、多义性、否定词处理及意见垃圾检测等情感分析挑战及其研究趋势。最后，展望了情感分析在教育领域的未来发展方向。