A Mood-based Genre Classification of Television Content

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Title: A Mood-based Genre Classification of Television Content
Authors: Corona, Humberto
O'Mahony, Michael P.
Permanent link: http://hdl.handle.net/10197/7545
Date: 10-Oct-2014
Online since: 2016-04-05T08:54:31Z
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 Box- fish 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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2014 ACM
Keywords: Mood analysisText classificationGenre classification
Other versions: https://recsys.acm.org/recsys14/recsystv/
Language: en
Status of Item: Peer reviewed
Conference Details: ACM Workshop on Recommendation Systems for Television and Online Video, Foster City, California, USA, 6-10 October 2014
Appears in Collections:Insight Research Collection

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