The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has been widely and successfully applied in other fields such as music or blog posts. We use a real-world dataset obtained from the Boxfish API to compare the performance of classifiers trained on a number of different feature sets. Our experiments show that, over a large collection of television content, program genres can be represented in a three-dimensional space of valence, arousal and dominance, and that promising classification results can be achieved using features based on this representation. This finding supports the use of the proposed representation of television content as a feature space for similarity computation and recommendation generation.

本文介绍了利用从节目文本信息中提取特征进行电视内容分类的方法，研究表明，通过情感值，电视节目可在一个三维空间中表示，得到很好的分类结果，证明了该方法可用于推荐生成。

电视节目内容的基于情绪的流派分类