Learning to rank for joy

Abstract

User-generated content is a growing source of valuable infor- mation 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 affec- Tive 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 show that, while basic video fea- Tures, such as title and tags, lead to effective rankings in an affective-less setup, they do not perform as good when dealing with an affective ranking task.