Personalized MTV affective analysis using user profile

Abstract

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. © 2008 Springer.