Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis.