In this paper, we propose an approach for affective characterization of movie scenes based on the emotions that are actually felt by spectators. Such a representation can be used to characterize the emotional content of video clips in application areas such as affective video indexing and retrieval, and neuromarketing studies. A dataset of 64 different scenes from eight movies was shown to eight participants. While watching these scenes, their physiological responses were recorded. The participants were asked to self-assess their felt emotional arousal and valence for each scene. In addition, content-based audio- and video-based features were extracted from the movie scenes in order to characterize each scene. Degrees of arousal and valence were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based features. We showed that a significant correlation exists between valence-arousal provided by the spectator's self-assessments, and affective grades obtained automatically from either physiological responses or from audio-video features. By means of an analysis of variance (ANOVA), the variation of different participants' self assessments and different gender groups self assessments for both valence and arousal were shown to be significant (p-values lower than 0.005). These affective characterization results demonstrate the ability of using multimedia features and physiological responses to predict the expected affect of the user in response to the emotional video content.