文献类型：期刊文献

英文题名：Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment

作者：Tang, Wenzhe[1];Zhang, Fang[2]

机构：[1]Xian Siyuan Univ, Xian 330022, Shaanxi, Peoples R China;[2]Shaoxing Univ, Sch Civil Engn, Shaoxing 312000, Zhejiang, Peoples R China

年份：2022

卷号：2022

外文期刊名：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000829737800001)、、EI(收录号：20222412208031)、Scopus(收录号：2-s2.0-85131437984)、WOS

语种：英文

外文关键词：E-learning - Education computing - Support vector machines - Vector spaces - Vectors

外文摘要：The separation of time and space in immersive virtual teaching makes students unable to realize emotional communication, which may affect students' mental health. In recent years, the use of affective computing technology to solve the problem of affective loss in distance education has become a key research topic. In order to realize the problem of emotion interaction in immersive virtual teaching, a semisupervised support vector machine- (SVM-) based affective interaction model was proposed. First, the natural language sequences of students in the virtual teaching environment are preprocessed using a statistical-based framing method, and mutual information and expected cross-entropy are used as feature selection methods. Then, a vector space model based on TF/IDF feature term weights is proposed to implement the feature vector representation of natural language sequences. Finally, after the constructed sentiment space, a semisupervised SVM is employed as the classifier to complete the affective interaction computation. The experimental results of emotion classification show that the proposed model is able to determine and understand the emotional state more accurately than other traditional models and significantly improves the training speed. In addition, the proposed model can provide emotional encouragement or emotional compensation according to the specific emotional state of the learner.