发布时间：2025-04-30

论文名称：Recognizing and Regulating e-Learners' Emotions Based on Interactive Chinese Texts in e-Learning Systems

发表刊物：Knowledge-based Systems

摘要：Emotional illiteracy exists in current e-learning environment, which will decay learning enthusiasm and productivity, and now gets more attentions in recent researches. Inspired by affective computing and active listening strategy, in this paper, a research and application framework of recognizing emotion based on textual interaction is presented first. Second, an emotion category model for e-learners is defined. Third, many Chinese metaphors are abstracted from the corpus according to the sentence semantics and syntax. Fourth, as the strategy of active learning, topic detection is used to detect the first turn in dialogs and recognize the type of emotion in the turn, which is different from the traditional emotion recognition approaches that try to classify every turn into an emotion category. Fifth, compared with Support Vector Machines (SVM), Naive Bayes, LogitBoost, Bagging, MultiClass Classifier, RBFnetwork, J48 algorithms and their corresponding cost-sensitive approaches, Random Forest and its corresponding cost-sensitive approaches achieve better results in our initial experiment of classifying the e-learners’ emotions. Finally, a case-based reasoning for emotion regulation instance recommendation is proposed to guide the listener to regulate the negative emotion of a speaker, in which a weighted sum method of Chinese sentence similarity computation is adopted. The experimental result shows that the ratio of effective cases is 68%.

(DOI) http://dx.doi.org/10.1016/j.knosys.2013.10.019.

合写作者：田锋，高鹏达，李隆庄，张未展，梁慧军，骞亚楠，赵若萌

卷号：55

页面范围：148–164

是否译文：否

发表时间：2013-10-24