A review on sentiment discovery and analysis of educational big‐data. (11th July 2019)

• Record Type: Journal Article
• Authors: Han, Zhongmei; Wu, Jiyi; Huang, Changqin; Huang, Qionghao; Zhao, Meihua
• Is Part Of: Wiley interdisciplinary reviews. Volume 10:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-11
• Subjects: educational big‐data -- multimodal fusion -- sentiment discovery and analysis -- unimodal feature extraction; Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1328
• Languages: English
• ISSNs: 1942-4787

Abstract

Abstract: 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. This article is categorized under: Application Areas > Education and Learning Abstract : The techniques of SDA can be categorized into unimodal and multimodal for feature selection and sentiment classification. Based on the characteristics of education big‐data, the applications of these techniques focus on traditional classroom and e‐learning situations.