Classifying harmful children's content using affective analysis

This paper categorizes children's videos according to an expertly assigned predefined positive or negative cognitive impact category. The method uses affective features to determine if a video belongs to an expertly assigned predefined positive or to a negative cognitive impact category. The work demonstrates that simple affective features outperform more complex systems in determining if content belongs to the positive or negative cognitive impact category. The work is tested on a set of videos that have been classified as having a short term or long term measurable negative or positive impact on cognition based on cited psychological literature. It found that affective analysis had superior performance using less features than state of the art video genre classification systems. It also found that arousal features performed better than valence features.