Machine Learning Models for Content Classification in Film Censorship and Rating

Abstract

Automated Film Censorship and Rating (AFCR) has recently turned out to be a major research area of Machine Learning (ML). The production and streaming services of films including movies, tv-series, animations and other audio-visual contents have been widely expanded leading to their manual censorship and rating to be a more exhausting task. Development of ML based methods has thus been emerging to designing an AFCR system. However, the initial ad-hoc efforts of developing the AFCR system demand a “complete” conceptual model of the system with its potential classes and their criteria. This paper primarily attempts to determine both the general and contextual classes of the content, and their criteria for an AFCR system. Besides, the state-of-the-art AFCR systems have been systematically reviewed to identify their underlying ML models, advantages and limitations. With a comparative analysis of the exiting ML models, we have demonstrated the effectiveness of sequential and multimodal analysis in the development of an efficient AFCR system.