Improving classification accuracy of youtube videos by exploiting focal points in social tags

Past literature [1] has shown that problems involving tacit communication among humans and agents are better solved by identifying communication 'focal' points based on domain specific human biases. Cast differently, classification of user-generated content into generalized categories is the equivalent of automated programs trying to match human adjudged labels. It seems logical to suspect that identification and incorporation of features generally found salient by humans or 'focal points', can allow an automated agent to better match human adjudged labels in classification tasks. In this paper, we leverage this correspondence, by using domain-specific focal points to further inform the classification algorithms of the inherent human biases. We empirically evaluate our method, by classifying YouTube videos using user-annotated tags. Improvements in classification accuracy over the state-of-the-art classification techniques on using our transformed (using focal points) and highly reduced feature space reveals the value of the approach in subjective classification tasks.