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User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking. To this end, we follow a learning to rank approach, which allows us to compare the performance of different sets of features when the ranking task goes beyond mere relevance and requires an affective understanding of the videos. Our results showdoi:10.1145/2567948.2576961 dblp:conf/www/Orellana-RodriguezNDA14 fatcat:vzhe4tdezbavpkvebwm23isxwm