A Mood-based Genre Classification of Television Content

TL;DR

This paper proposes a mood-based approach to classify television content genres using sentiment analysis of program transcriptions, demonstrating that valence, arousal, and dominance features effectively distinguish genres.

Contribution

It introduces a novel application of sentiment analysis with a three-dimensional mood space for television genre classification, validated on real-world data.

Findings

Genres cluster in valence, arousal, dominance space

Mood features improve classification accuracy

Content similarity can be derived from mood representations

Abstract

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.