Learning from audience intelligence: Dynamic labeled LDA model for time-sync commented video tagging

Abstract

With the boom of online video uploading, video tagging becomes an important way for video indexing. However, text-based video tagging methods ignore either genre labels or temporal differences of videos, which makes results defective. Fortunately, a new type of videos called time-sync commented videos which contains large amounts of information commented by the users helps videos tagging. In this paper, we propose a supervised dynamic Latent Dirichlet Allocation model utilizing the variational topics of time-sync comments to extract both genre labels and keywords as tags. We also implement experiments on large scale real-world datasets and the effectiveness of our model are proved both in genre label classification and keyword extraction compared with baseline models.