Affective computing
A vector space model for automatic indexing
Communications of the ACM
Foundation Analysis of Emotion Model for Designing Learning Companion Agent
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
A BDI approach to infer student's emotions in an intelligent learning environment
Computers & Education
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Adding redundant features for CRFs-based sentence sentiment classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Handbook of Natural Language Processing
Handbook of Natural Language Processing
A survey of Chinese text similarity computation
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Chinese Sentence-Level Sentiment Classification Based on Sentiment Morphemes
IALP '10 Proceedings of the 2010 International Conference on Asian Language Processing
Predicting consumer sentiments from online text
Decision Support Systems
Visualizing e-Learner Emotion, Topic, and Group Structure in Chinese Interactive Texts
ICALT '11 Proceedings of the 2011 IEEE 11th International Conference on Advanced Learning Technologies
An overlay multicast protocol for live streaming and delay-guaranteed interactive media
Journal of Network and Computer Applications
Gesture-Based affective computing on motion capture data
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Emotional metaphors for emotion recognition in chinese text
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Emotion estimation and reasoning based on affective textual interaction
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
An Affective Learning Interface with an Interactive Animated Agent
DIGITEL '12 Proceedings of the 2012 IEEE Fourth International Conference On Digital Game And Intelligent Toy Enhanced Learning
CISIS '12 Proceedings of the 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS)
IEEE Transactions on Affective Computing
Context-Sensitive Learning for Enhanced Audiovisual Emotion Classification
IEEE Transactions on Affective Computing
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Harnessing Twitter "Big Data" for Automatic Emotion Identification
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Frontiers of Affect-Aware Learning Technologies
IEEE Intelligent Systems
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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%.