Affective Recommendation of Movies Based on Selected Connotative Features

The apparent difficulty in assessing emotions elicited by movies and the undeniable high variability in subjects' emotional responses to film content have been recently tackled by exploring film connotative properties: the set of shooting and editing conventions that help in transmitting meaning to the audience. Connotation provides an intermediate representation that exploits the objectivity of audiovisual descriptors to predict the subjective emotional reaction of single users. This is done without the need of registering users' physiological signals. It is not done by employing other people's highly variable emotional rates, but by relying on the intersubjectivity of connotative concepts and on the knowledge of user's reactions to similar stimuli. This paper extends previous work by extracting audiovisual and film grammar descriptors and, driven by users' rates on connotative properties, creates a shared framework where movie scenes are placed, compared, and recommended according to connotation. We evaluate the potential of the proposed system by asking users to assess the ability of connotation in suggesting film content able to target their affective requests.