Aesthetic highlight detection is a challenge for understanding affective processes underlying emotional movie experience. Aesthetic highlights in movies are scenes with aesthetic values and attributes in terms of form and content. Deep understanding of human emotions while watching movies and automatic recognition of emotions evoked by watching movies are critically important for a wide range of applications, such as affective content creation, analysis, and summarization.|Many empirical studies on emotions have formulated theory-driven and data-driven models to uncover the underlying mechanism of emotions using discrete ad dimensional paradigms. Nevertheless, these approaches to emotions do not fully reveal all underlying processes of emotional experience. Recent neuroscience findings has led to the development of multi-process frameworks that aim to characterize emotions as a multi-componential phenomena. In particular, multi-process frameworks can be useful to study emotional movie experience.|In this work, we carry out statistical analysis of the componential paradigm on emotions while watching aesthetic highlights in full-length movies. We focus on the effect of the aesthetic highlights on intensity of emotional movie experience. We explore occurrence frequency of different semantic categories involved in constructing different types of the aesthetic highlights. Moreover, we investigate the applicability of machine learning classifiers in predicting the aesthetic highlights from movie scene semantics based features.