WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Affect computing in film through sound energy dynamics
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
User Modelling for News Web Sites with Word Sense Based Techniques
User Modeling and User-Adapted Interaction
A tutorial on support vector regression
Statistics and Computing
Personalized multimedia retrieval: the new trend?
Proceedings of the international workshop on Workshop on multimedia information retrieval
Proceedings of the 15th international conference on Multimedia
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Affective video content representation and modeling
IEEE Transactions on Multimedia
Affective understanding in film
IEEE Transactions on Circuits and Systems for Video Technology
Correlation-based feature selection and regression
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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At present, MTV has become an important favorite pastime to people. Affective analysis which can extract the affective states contained in MTVs could be a potential and promising solution for efficient and intelligent MTV access. One of the most challenging and insufficiently covered problems of affective analysis is that affective understanding is personal and various among users. Consequently, it is meaningful to develop personalized affective modeling technique. Because user's feedbacks and descriptions about affective sates provide valuable and relatively reliable clues about user's personal affective understanding, it is supposed to be reasonable to conduct personalized affective modeling by analyzing the affective descriptions recorded in user profile. Utilizing the user profile, we propose a novel approach combining support vector regression and psychological affective model to achieve personalized affective analysis. The experimental results including both user study and comparisons between current approaches illustrate the effectiveness and advantages of our proposed method.