Recognizing and regulating e-learners' emotions based on interactive Chinese texts in e-learning systems
Recognizing and regulating e-learners' emotions based on interactive Chinese texts in e-learning systems
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DOI：
10.1016/j.knosys.2013.10.019
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发表时间：
2014-01-01
影响因子：
8.8
通讯作者：
Zhao, Ruomeng
中科院分区：
文献类型：
--
作者：
Tian, Feng;Gao, Pengda;Zhao, Ruomeng
当前网络学习环境中存在着情绪文盲现象，它会降低学习积极性和学习效率，近年来越来越受到研究人员的关注。受情感计算和主动倾听策略的启发，本文首先提出了一种基于文本交互的情感识别研究与应用框架。其次，定义了面向网络学习者的情感分类模型。第三，许多汉语隐喻是根据句子语义和句法从语料库中抽象出来的。第四，作为主动学习的策略，话题检测用于检测对话中的第一个回合，并识别该回合中的情感类型，这与传统的情感识别方法试图将每个回合都归类为情感类别不同。第五，与支持向量机（SVM）、朴素贝叶斯（Naive Bayes）、LogitBoost、Bagging、MultiClass Classifier、RBFnetwork、J48算法及其相应的代价敏感方法相比，随机森林及其相应的代价敏感方法在我们初步的电子学习者情绪分类实验中取得了更好的效果。最后，提出了一种基于案例推理的情绪调节实例推荐方法，该方法采用中文句子相似度加权和计算方法，引导听者调节说话人的负面情绪。实验结果表明，有效病例率为68%。(C) 2013 Elsevier B.V.版权所有
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%. (C) 2013 Elsevier B.V. All rights reserved.