Abstract
In this work, we develop deep neural networks for predicting affective responses from movies taking both audio and video streams into account. This study also tackles the issue of how to build a representation of video and audio in order to predict emotions that movies elicit in viewers. Besides, we analyse and identify helpful features extracted from video and audio streams that are important for the design of a good emotion prediction model. Fusion techniques are also taken into account with the aim to obtain the highest prediction accuracy.
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