Social network analysis of TV drama characters via deep concept hierarchies

Abstract

TV drama is a kind of big data, containing enormous knowledge of modern human society. As the character-centered stories unfold, diverse knowledge, such as economics, politics and the culture, is displayed. However, unless we have efficient dynamic multi-modal data processing and picture processing methods, we cannot analyze drama data effectively. Here, we adopt the recently proposed deep concept hierarchies (DCH) and convolutional-recursive neural network (C-RNN) models to analyze the social network between the drama characters. DCH uses multi hierarchies structure to translate the vision-language concepts of drama characters into diversified abstract concepts, and utilizes Markov Chain Monte Carlo algorithm to improve the retrieval efficiency of organizing conceptual spaces. Adopting approximately 4400-minute data of TV drama - Friends, we process face recognition on the characters by using convolutional-recursive deep learning model. Then we establish the social network between the characters by deep concept hierarchies model and analyze their affinity and the change of social network while the stories unfold.