Sentiment discovery and analysis (SDA) aims to automatically identify the underlying attitudes, sentiments, and subjectivity towards a certain entity such as learners and learning resources. Due to its enormous potential for smart education, SDA has been deemed as a powerful technique for identifying and classifying sentiments from multimodal and multisource data over the whole process of education. For big educational data streams, SDA faces challenges in unimodal feature selection, sentiment classification, and multimodal fusion. As such, a large body of studies in the literature explores diverse approaches to SDA for educational applications. This paper provides a self‐contained, uniform overview of the SDA techniques for education. In particular, we focus on prominent studies in unimodal sentiment features and classifications (e.g., text, audio, and visual). In addition, we present a novel SDA framework of multimodal fusions, together with description of their crucial components. Based on this framework, we review different approaches to SDA on education from the perspectives of approaches and applications. After comprehensively reviewing the SDA techniques on education, we present the trends and prospectives of the future SDA research under ubiquitous education contexts.

中文翻译：

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教育大数据情感发现与分析研究述评

情感发现和分析（SDA）旨在自动识别对某些实体（例如学习者和学习资源）的潜在态度，情感和主观性。由于其在智能教育中的巨大潜力，SDA被认为是一种在整个教育过程中从多模式和多源数据中识别和分类情感的强大技术。对于大型教育数据流，SDA在单峰特征选择，情感分类和多峰融合方面面临挑战。因此，文献中的大量研究探索了用于教育应用的SDA的多种方法。本文提供了SDA教育技术的独立，统一的概述。特别是，我们将重点放在单峰情感特征和分类（例如文本，音频和视频）。此外，我们提出了一种新颖的多模式融合SDA框架，并对其关键组件进行了描述。在此框架的基础上，我们从方法和应用的角度回顾了不同的教育SDA方法。在全面回顾了SDA教育技术之后，我们介绍了在无所不在的教育环境下未来SDA研究的趋势和前景。