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:AKA 意见采矿是用于确定审查中人类意图的最广泛应用的NLP应用软件之一。在教育部门,意见采矿用于听取学生的意见,并用教学方法加强他们的学习方法。随着情绪批注技术和AI方法的进步,学生的意见可以被贴上情感取向的标签,而没有太多的人力干预。在审查文章中,(1) 我们认为情感分析在教育中具有四个层次的作用:文件级别、判刑级别、实体级别和层面;(2) 情绪批注技术,包括基于词汇和基于实体的方法,用于不受监督的说明;(3) 探讨AI在情感分析中的作用,包括机器学习、深层次学习和变异器等方法;(4) 情绪分析对教育程序的影响,以加强教学、决策和评估; 教育机构已广泛投入建设情绪分析工具,处理学生反馈,以征求他们的意见和见解; 本研究中审查了学生情绪分析的应用。在情绪分析方面面临的挑战,如多极性、多元性、否定性、否定性的语言和变异性分析;在空间感学研究中讨论了其研究趋势和见解分析方向。