Research On Learner’s Emotion Modeling And Its Application In E-Learning

In recent years, with the rapid development of multimedia technology and the Internet, e-Learning has become another important way for learning. E-Learning breaks the limitation of time and space in traditional teaching mode, and provides learners with a loose, free and open learning environment. However, in e-Learning environment, learners mostly learn by themselves, which result in the feeling of isolation. That leads learner’s lack of emotional support, and then limits the effect of e-Learning.To give emotional support for learners, we should develop an intelligent learning system which can understand learners’ emotion and give them some appropriate responses. Learner Model, as one of the most important components of intelligent learning system, stores the individual characteristics of learners, which are the basis of implementing personalized and adaptive learning service. Therefore, modeling learner’s emotion is of great value in powering the intelligent learning system with emotional capability.Micro-blog is currently the most popular online social network, with simplicity, convenience and instantaneity, widely used by the young students. It provides learners with not only a simple, instant way for communication, but also a free space for emotion expression. So it can become a new way of modeling learners’ emotion. However, learners’ micro-blogs usually include many non-emotional data, and the content is very short, and the syntax is also very loose, which bring many challenges for emotion mining from learner’s micro-blogs.Around modeling learner’s emotion, this paper focus on the emotion modeling technologies based on analysis of his/her micro-blogs. The main research content includes five aspects:(1) The learner model that integrated emotion characteristics;(2) The recognition of learner’s emotional micro-blogs;(3) The description of emotional semantic in learner’s micro-blogs;(4) The emotion classification algorithm for learner’s micro-blog;(5) The implementation and application of learner emotion modeling. Based on the above studies, the contributions of this thesis are mainly reflected in the following aspects:(1)Aiming at that the characteristics of existing learner models are not systematic, especially do not consider the learner’s emotion, we put forward a learner model with emotion characteristic and an emotion modeling framework based on micro-blog analysis in this dissertation. The learner model includes five characteristics of learners, and offers their formalization description respectively. As for the formalization description of learner’s emotion, based on the category model and space model in psychology, a space model using the basic emotions as the dimensions was proposed. Furthermore, we analyzed the feasibility and advantages of micro-blog for learner’s emotion modeling, and proposed a learner emotion modeling framework.(2)As there are much noise data in learners’ micro-blogs, we study on the automatic recognition of emotional micro-blogs. Generally, we can use a binary classification algorithm to classify the micro-blogs into emotional class or non-emotional class, but it is hard to form a statistically-representative sample set of the non-emotional class. So we proposed a new approach based on one-class classification to recognize the emotional micro-blogs. First, based on the analysis of a large number of non-emotional micro-blogs, we summarized five rules to filter out the most easily identifiable non-emotional micro-blogs. Then26features were selected to represent the micro-blogs and the one-class classification algorithm was used to further recognize the emotional micro-blogs.(3)The learners’ micro-blogs are usually very short. So if we use the general vector space model based on keywords to describe the emotional semantic of micro-blogs, it will lead to a very large, sparse feature space. In order to resolve this problem, we proposed a new approach based on words clustering to describe the emotional semantic of micro-blogs. First, we selected the words which are useful for emotion expression from the micro-blogs corpus by three strategies:stop words filtering, low-frequency words filtering and filtering based on the information gain. Then we computed the semantic similarity of words based on their contexts, and proposed a hierarchical clustering algorithm to group words into several clusters according their semantic similarity. Finally, we used the word clusters as features, and proposed a feature weighting method which includes three factors:the sum of weighting values of all component words, the representation degree of component words, and the discrimination degree of word cluster.(4)There are many classification algorithms, but most existing studies only use one algorithm to predict the emotion category of micro-blogs, and do not leverage the complementarities between them. To improve the emotion classification accuracy of learners’ micro-blogs, an ensemble approach based on meta-learning was proposed. First, we respectively used the Naive Bayes, Maximum Entry and Support Vector Machine to learn three component classifiers from the micro-blog corpus. Then we employed the stacking method and N-fold cross validation to produce the training samples for meta-learning, and used the Maximum Entry or Support Vector Machine to learn a meta-classifier. Finally, we used the component classifiers and the meta-classifier to predict the emotion category of micro-blogs successively, and the result of the meta-classifier is the final result.